alireza shakiba

The most important pillar of a scientific and applied research is the statement of the problem, when a problem can be scientific and practical that creates a challenge in relation to the solution of the problem and clearly defines the purpose of the work, as well as the challenges that have arisen in relation to the problem in question, the researcher uses Simulation models can overcome one of the challenges and work as a source of information for climate researchers to use for future research. In this research, the statement of the present problem is the statement of forecasting the average seasonal temperature. What element is temperature, the answer to these questions and reasons, let's hypothesize against it, after formulating the assumptions, prepare climatic data of temperature of the study area and the neighboring stations of the area, and also specify the study area To start the work, using modeling (simulation) and comparison and accuracy of forecasting, he used two models by comparing and measuring the accuracy of their errors, because Temperature is a physical quantity, some of the sun's radiant energy is absorbed by the earth's surface and becomes thermal energy.This energy is expressed in the form of temperature or degrees. Among the different climatic elements, temperature and precipitation are of special importance to predict this. The important key climatic element, our goal is to examine the seasonal average temperature changes in the seasons and determine the seasonal changes with 95% and 80% accuracy using artificial neural network - Arima time series model, RMSE index, and also the models together Let's compare which predicts temperature changes better. So that researchers can use and test these models in future researches to predict other climate parameters and also the impactful consequences of seasonal temperature changes and climate elements such as relative humidity - evaporation and transpiration - industry - transportation - bridges and other infrastructures. Proper planning and management should be done in this regard. In the 21st century, climate change is considered one of the biggest environmental threats to the world. Changes in Farin's climate are estimated to have more negative effects on human society and the natural environment than changes in the average climate (Mahmood and Babel, 2014: 56). Based on the fourth report of the International Commission on Climate Change, which was published under the title of Climate Change Assessment Reports, the global increase in temperature and the occurrence of climate change have been confirmed by using the measured data of the surface temperature of land and water in the world (IPC Si, 2014: 32). The first effect of climate change on atmospheric elements is especially temperature and precipitation, then due to the relationship between atmospheric elements and terrestrial ecosystems, water resources, vegetation, soil and also human life will be affected by this phenomenon; Therefore, investigating the trend of atmospheric variables such as temperature is of particular importance (Abkar et al., 2013: 14).Temperature Some of the radiant energy of the sun absorbed by the effects of the earth's surface turns into thermal energy. This energy is manifested in the form of temperature or degree. Among the different climatic elements, temperature and precipitation are of particular importance. Although the main cause of temperature is the energy obtained from the absorption of short solar radiation on the earth's surface.Using artificial neural network and Arima time seriesThe purpose of this research is to model forecasting changes in seasonal average temperature in the study area of Al-Shatar city using artificial neural network and Arima time series model and to determine the measurement accuracy of neural network models and Arima time series model in forecasting average temperature changes and also The above simulation models should be used to predict the research of future climate researchers and be realized.The main goals of this research are to model and identify seasonal average temperature changes and the relationship of this key element with other climatic parameters of Al-Shatar city. In terms of seasonal average temperature changes and prioritizing areas with temperature variability.This part of the research has monitored and simulated the regression error of Lorestan stations (Alshatar-Broujerd-Aligodarz-Noorabad-Khorramabad-Poldakhter) in the time period (1998-2018) of the stations of Lorestan province with the temporal-spatial analysis of the RMSE index. The obtained results show that the indicators of the cold period of the year in the current situation in different areas (stations of Lorestan province) have had different trends, but the average temperature of the cold seasons of autumn and winter is an increasing trend, which results in the melting of the glaciers and snowfall. Rain is coming and this process is predicted for the next eleven (11) years. In general, the results obtained in this section have shown that the heat waves in the future will be more intense, sharper and more lasting than the current situation, and the highest temperature fluctuations in the autumn season, which is 81. Using RMSE = .003 and ME = .86, it is the artificial neural network that has the best efficiency and performance in Elshatar city station and predicts the average temperature better than the Arima time series model, therefore the artificial neural network model and Arima time series Both have 95% and 80% measurement accuracy. It is better to use these models and other machines in future research to predict the minimum and maximum temperature and other climatic elements. Because they have the best performance and efficiency in forecasting the elements, forecasting the average seasonal temperature can help to plan and manage, control evaporation and transpiration and other resources of the country and Al-Shatar city. It is summer and the least accuracy is in autumn and winter. Considering that the prevailing rain-producing air masses in Al-Shatar city leave the most seasonal changes in autumn and winter, it can be concluded that the most temperature fluctuations occur in the cold seasons of the year and the least fluctuations in The summer season occurs due to the deactivation of the rain-producing western wind, whose value is 33/. Is

Climate change is considered one of the most significant challenges facing humanity. This phenomenon has had notable impacts on agricultural production in most parts of the world, especially in arid and semi-arid regions. The average temperature has also increased in many areas over the past few decades. Today, various studies utilize remote sensing indices as one of the modern methods for identifying climate change. One of the important remote sensing indices is the phenology characteristics of vegetation cover, which has demonstrated a promising ability in identifying and estimating vegetation cover in recent studies.
The five-day time series of the Normalized Difference Vegetation Index (NDVI) from NOAA-AVHRR images and plant phenology parameters were utilized to examine changes in vegetation cover in rangeland and rainfed agricultural lands of the Urmia Lake basin over the years 1984-2013. Temperature and precipitation data were obtained from meteorological stations in the Urmia Lake basin and were used in comparison with satellite image results.
The results of the time series analysis over the thirty year period in the Urmia Lake basin revealed that the start of the growing season in Oshnavieh, Saqqez, and Sarab areas in 2013 commenced earlier than in 1984, while in the Maragheh area, it started later. The end of the growing season in Oshnavieh, Saqqez, and Takab ended earlier, and based on the peak growth parameter, vegetation in these counties reached its maximum value earlier. The results also showed the length of the growing season in Oshnavieh, Maragheh, and Saqqez counties has shortened, respectively.
The results obtained from satellite images and climate data indicated that the  observed changes in phenology parameters are location-dependent. Cold nights and warm days at the start of the growing season decreasing and increasing, respectively. However, warm days increased at the end of the growing season. These changes have increased the slope of the plant growth phenology curve during the senescence period, ultimately reducing the length of the growing season.

Climate change, including spatial changes in rainfall following the increase in greenhouse gases, is a challenge that affects various aspects of life in human societies today. In this study, the long-term spatial changes of the rainy season from September to May were studied using the statistics of "local Moran" and "Hot spots of Getis Ord-J" during 5 5-year periods from 1991 to 2015. The application of local Moran statistics showed that areas with long rainfall periods are in good agreement with the countrychr('39')s rainfall pattern. This adaptation is accompanied by a negative spatial correlation in lands with short rainfall periods. The pattern obtained from the hot spot statistics also showed itself to be more consistent and corresponded to two very low rainfall regimes in the southeast and center, while according to local Moran statistics, this pattern was more scattered and parts of it had a low southern precipitation regime. . In addition, the results of Alexandersonchr('39')s statistics to identify mutations in the long-term series of the rainy season showed that the time series of 13 stations out of 108 stations studied experienced a sudden jump that these mutations are more in the southern stations in the country and in later years. It has occurred since 2000 AD.

The geopolitical dynamics of the Middle East have always been influenced by the competition among global powers, particularly the United States and China. This study aims to analyze how the US-China rivalry from 2010 to 2022 has impacted Iran's national security. The region's strategic significance and its vast energy resources make it a focal point for these powers, each seeking to expand their influence. Iran, as a key player in the Middle East, finds its security and political landscape significantly affected by this rivalry. The research hypothesizes that the competition between the US and China affects Iran's national security in three main domains: military/security, political, and economic, with both positive and negative impacts.

This research adopts an explanatory method, relying on extensive library and documentary data to dissect the multi-faceted impacts of US-China competition on Iran's national security. The study involves a thorough review of historical and contemporary sources to establish a comprehensive understanding of the geopolitical strategies employed by the US and China in the Middle East and their repercussions on Iran. The analysis is structured to address the competition in the specified domains, offering a detailed examination of each domain to discern the dual effects on Iran's security framework.

The findings reveal a complex interplay of influences stemming from the US-China rivalry. In the military/security domain, the competition has generally been detrimental to Iran's national security. Increased US military presence and alliances in the region pose direct threats to Iran, while China's military engagements offer limited but notable strategic support to Iran, such as joint military maneuvers and arms sales. However, China's broader military relations with other regional actors like Saudi Arabia and Israel introduce significant security dilemmas for Iran. Economically, the US's efforts to isolate Iran through sanctions have been countered to some extent by China's continued economic engagements with Iran, including the 25-year cooperation agreement. Nevertheless, China's extensive economic ties with Iran's regional rivals diminish these benefits and perpetuate the economic vulnerabilities due to continued and even reinforced US sanctions. Politically, the situation is somewhat more favorable for Iran. China's refusal to align with US policies aimed at isolating Iran, such as opposing additional sanctions in the United Nations, provides Iran with crucial diplomatic support. Additionally, China's support for the Assad regime in Syria aligns with Iran's strategic interests in the region, reinforcing Iran's influence in the Levant. However, the growing US-Arab-Israeli rapprochement, facilitated by the Abraham Accords, and the US's emphasis on human rights issues against Iran complicate Iran's geopolitical stance.

The US-China competition in the Middle East significantly impacts Iran's national security. Militarily and economically, the rivalry presents more threats than opportunities, primarily due to US actions aimed at containing Iran and China's balanced approach to regional partnerships. Politically, however, the competition offers Iran some leverage, as China's opposition to US-led initiatives provides Iran with critical support. Understanding these dynamics is essential for Iran to navigate its foreign policy and security strategies effectively amidst the ongoing great power rivalry. This research underscores the need for a nuanced approach in analyzing the impacts of global geopolitical competitions on regional actors like Iran, highlighting the dual nature of these influences across different domains.

Atmospheric rivers (ARs) are integrated and concentrated moisture paths that can influence precipitation in different countries. In this research, we aim to quantify the spatial distribution of ARs for 147 synoptic stations in Iran, their precipitation contributions, and their relation to total precipitation for six cold months of the year for 25 years (1995-2020). In this regard, the atmospheric rivers were identified based on the geographical location of each station and its precipitation characteristics. Then statistical analysis was used to determine the precipitation share and spatial distribution. As a result, on average, 8% of all studied times are associated with atmospheric rivers throughout the country. In addition, approximately 51% of the events identified have resulted in precipitation. In terms of location, most of the stations on the western slopes of Zagros and some coastal stations in the south have experienced all these events as rainy. This phenomenon is estimated to have provided between 6 and 69 percent of the country's total rainfall during six months of cold weather. There is a maximum share of this in the country's southern regions, and this share has decreased with the increase in latitude. Furthermore, the rise in AR precipitation is associated with an increase in total monthly rainfall in 84% of the studied stations. There is a spatial dissimilarity in the rain-affected by the phenomenon at different times.

The urban heat island (UHI) as a climatic effect of urbanization can negatively impact the flora and fauna involved in urban and suburban ecosystems, the presence of pollutants, air quality, energy and water consumption, as well as human health and economy. Therefore, spatiotemporal analysis of the urban heat island changes has been considered an effective approach to understanding the impact of urbanization on the urban and suburban ecosystem, which also can support sustainable urban development and planning. Accordingly, this study contributes a novel approach to identifying the trend and predicting the pattern of UHI changes using statistical analysis, Shannon's entropy and chi-score statistics.

The study area of this research is the city of Rasht and its surroundings, a region located in the north of Iran. This research was implemented using remote sensing images from 1991 to 2021 that were collected by LANDSAT 5 and 8 with a fixed time interval of 10 years. All images were captured in the summer. In order to conduct this research in the pre-foresight stage, first, the required preprocessing, including atmospheric and radiometric corrections applied to the satellite images. Then, the surface biophysical characteristics of the study area were extracted from the satellite images. In the third step, the land surface temperature was computed using satellite images in 2021. In the fourth step, Multivariate linear regression between surface biophysical characteristics and the land surface temperature in 2021 was applied and then the cellular automata-Markov chain model was utilized to predict the land surface temperature for 2031. Finally, the pattern of changes in the urban heat island of Rasht city was investigated using statistical analysis in different geographic directions and different time periods.

The results of this study indicate that the highest positive correlation (R=0.89) was between NDBI and LST. Moreover, the highest negative correlation (R=-0.81) was between the greenness and LST. Our results also showed that the lowest correlation (R=0.42) was between the brightness and LST. The predicted LST corresponding to surface biophysical characteristics using a multivariate linear regression model illustrates the low error of this approach (RMSE=1.33K) in 2021. This means that the predicted values in 2021 are close to the real values, and therefore, this model can be trusted to predict LST in 2031. Statistical analysis of the patterns of observed and expected changes in UHI clearly illustrated that Rasht urban expansion and the UHI expansion will consistently continue to increase from 1991 to 2031. However, the expansion rate changes over time and space. Moreover, these analyses also showed that the UHI of Rasht city has a high degree of freedom and a high degree of sprawl. Thus, and as a result, its degree of goodness is negative.

The pattern of UHI changes is highly dependent on the pattern of built-up land changes: as a result, sustainable development, resilience and environmental protection of Rasht requires direct monitoring and control of the pattern of urban growth, such as preventing changes in built-up areas and agricultural lands in suburban areas by incorporating a vertical form of development as well as constructing green roofs and walls and using high-reflectance building materials.

Nowadays, heavy rains are an important aspect of climate studies because of their dangers and damage. Cut-off lows are influential in the occurrence of heavy rainfall in Iran by creating atmospheric instability. In this regard, first, the climatic variables of temperature, precipitation, and geopotential height of 500 hPa were investigated in connection with the formation of cut-off low during the period 1986-2018. Then, to predict the changes of the cut-off low event in the future and its impact on heavy precipitation, ECMWF database ERA-Interim version and CMIP5 models with CORDEX exponential microscale approach under two scenarios of optimistic release RCP4.5 and pessimistic RCP8. 5 was used until 2099. The results showed that in the RCP4.5 scenario, the increase in precipitation was estimated to be at least 2.41 mm, and in the RCP8.5 scenario, the maximum precipitation decrease was estimated to be 91 mm. The surface pressure anomalies of 500 hPa will increase by 61.5 and 92.5 geopotential meters based on RCP4.5 and RCP8.5. The lowest frequency cut-off low events was in 1995 and 1996 with 10 events, and the highest event was in 1986 with 30 events. With the increase of air temperature and the advance of subtropical high pressure towards high latitudes, the place of formation and the path of movement cut-off low in 33 years have been extended towards northern latitudes, which indicate the effects of change of the climate in Iran. The existence of a significant positive relationship between the frequency cut-off low events and heavy rainfall was confirmed at the 5% error level. Moreover, the impact of cut-off low on the frequency of heavy rainfall in Iranian stations, especially the northern stations, has been more impressive.

The Middle East region has always been the scene of conflict between great powers. This conflict has been considered due to the importance of this region from the point of view of energy supply and geopolitical equations for these powers. With the passing of the Cold War and the arrival of the digital age, the arenas of competition between the great powers also underwent a transformation, however, the competition between the great powers, especially the US and China, in this region has received little attention despite its high importance. In order to determine the status of this competition, we had to survey the development process of the region during the period of 2010 to 2022 to find various aspects of this competition. Here, the main question of our research is, to what extent has the competition between US and China been pursued in the Middle East? The hypothesis is that the competition between these two powers in the Middle East region was not limited to one dimension, and for this reason, in order to achieve a more comprehensive view, in this paper, we discussed the three basic dimensions of this competition such as political/ diplomatic, economic and military/ security competition. Our findings indicated the expansion of the fields of competition in all three sectors. Competition in economic dimensions for China and competition in military and security fields were more important for the United States. In this research, a descriptive-analytical method has been used, relying on documentary-library data.

Beng today, human bioclimatic studies are the basis of urban planning, housing, architecture and more.In all of these studies, attention is paid to the interiors and its influence on the climate of the area in question, so tat the climate in turn influences the design of the building. The research metod in this study is based on Evanz modeling and using data Fifty Years (1966-2010) is the Ahvaz Synoptic Weather Station.And human comfort or lack of comfort in this climate was investigated based on the above modeling. The results show that the city of Ahnaz enjoys very hot to cool conditions throughout the year. It is out of the climate comfort zone during the winter and summer reasons and with the onset of spring and autumn seasons in the transition from cold to heat (April) and heat to cold (November) the climate of Ahvaz is approaching human conditions. Summary, it results that approximately9months of the year during the night without the use of mechanical devices and merely considering the materials appropriate to the climate and olso consider how elements in the city of Ahvaz are comfortable condition.Beng today, human bioclimatic studies are the basis of urban planning, housing, architecture and more.In all of these studies, attention is paid to the interiors and its influence on the climate of the area in question, so tat the climate in turn influences the design of the building. The research metod in this study is based on Evanz modeling and using data Fifty Years (1966-2010) is the Ahvaz Synoptic Weather Station.And human comfort or lack of comfort in this climate was investigated based on the above modeling. The results show that the city of Ahnaz enjoys very hot to cool conditions throughout the year. It is out of the climate comfort zone during the winter and summer reasons and with the onset of spring and autumn seasons in the transition from cold to heat (April) and heat to cold (November) the climate of Ahvaz is approaching human conditions. Summary, it results that approximately9months of the year during the night without the use of mechanical devices and merely considering the materials appropriate to the climate and olso consider how elements in the city of Ahvaz are comfortable condition.Beng today, human bioclimatic studies are the basis of urban planning, housing, architecture and more.In all of these studies, attention is paid to the interiors and its influence on the climate of the area in question, so tat the climate in turn influences the design of the building. The research metod in this study is based on Evanz modeling and using data Fifty Years (1966-2010) is the Ahvaz Synoptic Weather Station.And human comfort or lack of comfort in this climate was investigated based on the above modeling. The results show that the city of Ahnaz enjoys very hot to cool conditions throughout the year. It is out of the climate comfort zone during the winter and summer reasons and with the onset of spring and autumn seasons in the transition from cold to heat (April) and heat to cold (November) the climate of Ahvaz is approaching human conditions. Summary, it results that approximately9months of the year during the night without the use of mechanical devices and merely considering the materials appropriate to the climate and olso consider how elements in the city of Ahvaz are comfortable condition.Beng today, human bioclimatic studies are the basis of urban planning, housing, architecture and more.In all of these studies, attention is paid to the interiors and its influence on the climate of the area in question, so tat the climate in turn influences the design of the building. The research metod in this study is based on Evanz modeling and using data Fifty Years (1966-2010) is the Ahvaz Synoptic Weather Station.And human comfort or lack of comfort in this climate was investigated based on the above modeling. The results show that the city of Ahnaz enjoys very hot to cool conditions throughout the year. It is out of the climate comfort zone during the winter and summer reasons and with the onset of spring and autumn seasons in the transition from cold to heat (April) and heat to cold (November) the climate of Ahvaz is approaching human conditions. Summary, it results that approximately9months of the year during the night without the use of mechanical devices and merely considering the materials appropriate to the climate and olso consider how elements in the city of Ahvaz are comfortable condition.

One of the major manifestations of climate change in recent decades is the change in precipitation in different parts of the world. Since this climate parameter has a rebellious nature, recognizing its oscillating behavior is a necessity in the field of environmental planning in order to achieve sustainable development. Spectral analysis technique is an efficient method of identifying and understanding the overt and covert behavior of climatic elements and extracting and analyzing climatic fluctuations. Therefore, in the present study, using the daily precipitation data of 108 synoptic and rain gauge stations in the country, first the beginning and end of precipitation periods in the wet season (September-May) in 1990-2015 were extracted and then According to it, the longevity of the rainy season in the country was achieved in all stations. In the next step, using programming facilities in MATLAB software, the distribution of variances in all possible wavelengths at a significance level of 95%, in a time series of 26 years was obtained and finally in order to discover the spatial distribution of important and dominant cycles in software Arc Gis 10 software was mapped. The results showed that these fluctuations in the southern regions of the country are mainly 2 to 4 years and shorter and have less diversity and in the northern regions are more diverse and often 4 to 8 years. In general, significant fluctuations governing the longevity of the country's rainy season in the wet period of the year are mainly short-term and with a return period of 2 to 4 years, which is commensurate with large-scale fluctuations in oceanic climate indicators and is related to remote connection patterns. Also, the role of altitudes and distance and proximity to the sea in the diversity of cycles governing the northern and western half of the country is also significant.

The growing number and population of immigrants, especially after the Second World War, made different administrations pay special attention to immigration, its outcomes, and relevant policies. Meanwhile, the immigration policies of the United States of America have been much more dynamic and serious than those of other countries. These policies have consistently held special importance to every administration and political party in the US. In fact, the immigration policies of the US have historically been subject to change induced by both domestic and international conditions, incidents, events, and overarching national goals. Among the factors affecting the immigration policies of the US are the end of the Cold War, the September 11th attacks, the 2007–2008 financial crisis, the competitive atmosphere of the international system, and the shift away from a unipolar order. This study aims to address the following question: how effective is the administration’s affiliated party in changing immigration policies and laws? For this purpose, a qualitative-comparative approach was adopted in this paper to analyze the security and economic factors underlying immigration policies in the four elected administrations of Clinton, Bush, Obama, and Trump. The paper also aims to identify the similarities and differences in their approaches and the changes made to immigration laws and policies during their respective tenures. According to the research findings, the immigration policies of the US are not solely affected by the affiliated party. Rather, factors such as security and economic considerations are involved in shaping these policies.

Given that agriculture has the most important role in ensuring food security (Johnston & Kilby,1989), it is necessary to prepare a map that shows the spatial distribution, land area, and type of crops cultivated with high accuracy (Cai et al., 2018). Agricultural land cover is relatively dynamic and variable at relatively short intervals. This makes it difficult to classify crops on satellite imagery (Bargiel, 2017). The lack or absence of ground truth data is another cause. Therefore, methods that are less dependent on ground samples and use phenological features derived from time series of bands and vegetation indices to classify crops will be more appropriate (Ashourloo et al., 2020). The purpose of this study is to use a deep learning method based on convolutional networks to classify the crop types and improve the performance of this network by using feature channels as an input image to the network and increasing the classification accuracy.

In this study, the visible and near-infrared bands of Sentinel-2 satellite on 10 different dates from 2019 for an area in Idaho, USA, as an important agricultural area, and the cropland data layer for extracting the crop types ground labels was used (Han et al., 2012). Then, in MATLAB software, the time series of spectral bands were constructed and using them, temporal profiles of NDVI for any crop were extracted to identify the unique phenological features of crops. Then, the functions developed based on the phenological characteristics of crops were applied to the time series of the bands and a feature channel was obtained for each crop that in two separate processes, once bands and once again feature channels were used as input to the CNN and the network was trained and the results of network performance on crop classification in the test site, were compared.

In the first stage, the time series of bands formed the input of the deep convectional neural network and the network was trained in the training area, using the tempo-spectral information of bands as the input channels and crops ground samples as the related labels. Due to the spectral overlap of the crops in some time periods, network training was associated with a relatively high loss and therefore, for the test area, the overall classification accuracy was 69% (percent) and the kappa coefficient was 0.55. In the next step, the functions that were developed as phenological features for crops were applied on the time series of the bands, and for each crop, a feature channel was obtained as the special feature of that crop. Then the algorithm was implemented using these feature channels in the test area and the overall accuracy was upgraded to 86% and the kappa coefficient to 0.82 compared to which indicated a significant improvement in the results compared to the previous case.

The deep convolutional neural network is very sensitive to the type of input channels for detecting agricultural crops and selecting the channels with suitable tempo-spectral characteristics for different types of crops, has a great impact on the accuracy of network training and can reduce the loss of training network and increase its efficiency in the classification of various crops.

Earth's surface temperature is an important factor in global warming studies, and today it is the main challenge for many researchers worldwide. With remote sensing technology, it is possible to evaluate the temperature of the earth's surface and land use changes during different years with the help of satellite images, thermal infrared radiation, and physical models. In environmental studies, due to the location and location of the observations in the sample space, traditional statistics cannot be used due to the continuous structure of time and space. For this purpose, spatial statistics (spatial autocorrelation) is a suitable and new method for analyzing these data.

In this research, satellite data related to Landsat 5 and 8 images for the years 1990 and 2020 were obtained from the American Geological Survey. After correcting the images, the land use maps of Jiroft city were prepared and using the combination of visible and infrared bands, land use maps were prepared. The transformation of different land use classes and their changes during these years were analyzed in IDRISI software. Also, 150 control points from Google Earth were exploited to evaluate the accuracy of classified maps. In order to obtain the temperature of the earth's surface, the thermal bands of the received Landsat images were used. In two steps, the spectral radiance was converted to the temperature of the black body, and the surface temperature of the earth's surface was calculated. Finally, to reveal the spatial pattern of local differences, the local Moran's spatial autocorrelation statistic has been exerted.

The results showed that from 1990 to 2020, part of barren and flood channel lands was converted into water areas, which increased with the dam's construction after 1990. The average temperature of the earth's surface increased by 11.1 degrees in 30 years, which can be seen in all uses. The reason for this increase can be seen as the increase in air temperature. Another reason for the increase in the temperature of the earth's surface is the increase in construction in the region. The classification of the earth's surface temperature classes showed that the very hot and warm classes in the southern parts increased in 2020, and the average (most change) and cold classes decreased. The local spatial correlation statistics analysis results showed that hot clusters are gradually concentrated in southern regions and cool clusters in northern and northeastern regions.

The findings of this research showed that despite the increase in agricultural and garden use and the decrease in barren areas, the earth's surface temperature would increase to a large extent in all uses. Although manufactured areas are also increasing, the main reason for the temperature of the earth's surface can be considered the increase in air temperature and climate change.

Climate change is one of the current and future challenges facing agricultural producers, ie it affects the type and amount of products produced and ultimately the income of farmers. Therefore, it should be predicted or simulated in a way using various tools in the field of management of this work, so that according to it, the necessary planning and policy-making can be done, finally, while providing food security for consumption. Maximum welfare of producers should also be provided. Considering the high economic value of saffron crop and recent climate change, the need to provide an alternative cultivation pattern for current crops in our country, especially in Lorestan province, such as saifi and horticulture, which need plenty of water, is felt more than ever. Although Lorestan province ranks first in the country in terms of grain production and in terms of figs, pomegranates and other horticultural products is one of the relatively well-ranked provinces, but the climatic conditions of this province is a good ground for expanding and cultivating more productive and profitable crops. It also has another such as saffron.

The length of the statistical period required for climate analysis is at least 25-30 years. The statistical period from 1990 to 2016 was selected using meteorological data from synoptic stations. For spatial-temporal analysis of indices in the future, first, the average of 2050 statistical periods was calculated for two scenarios: RCP 4.5 and RCP 8.5 for temperature and precipitation indices. Then, each of the means was compared with the statistical period of the current situation. Finally, with Spline geostatistical method in GIS10.5 software, zoning maps of 2050 period were prepared based on two optimistic and pessimistic scenarios and future positive and negative phases were analyzed. Also, after preparing the data, the score of each component The weights of descriptive information of each component were determined according to their importance in saffron cultivation in the region. The weighting range of descriptive information was considered between 1-5. In the last stage, the components were combined with each other in GIS environment according to the score and weight of their inner layers, and the optimal areas of saffron cultivation in the current and future situation were identified based on pessimistic (RCP 8.5) and optimistic (RCP 4.5) scenarios. .

In the current situation, 15.8% of the province's area is in high priority areas, followed by relatively high and medium with 25.5% and 20.9%, respectively. These areas have different environmental constraints such as soil type, land use and unsuitable temperature. These areas can have saffron cultivation conditions with careful studies and adaptation measures. Relatively low priorities for saffron cultivation include mountainous areas with unsuitable temperature, slope and land restrictions. In the current situation, the most suitable cultivation areas for this crop are concentrated in the western and somewhat central areas of the province. Scattered areas in the northeast and north of the province are also suitable. Inappropriate areas include relatively low and low priorities in the eastern and southeastern areas. It is noteworthy that scattered areas in the southwest and north are also seen in two priorities. Future simulations show that climate change has a significant effect on the spatial change of suitable areas for saffron cultivation. By 2070, in the best conditions, according to the RCP scenario, 4.5 high-potential areas (high priority) will decrease by 1.5 percent, and areas with medium priority will increase by 4.8 and 6.3 percent, respectively. Without considering adaptation strategies and adopting appropriate management approaches in accordance with the RCP 8.5 scenario, saffron yield decreases due to climate change when temperature thresholds increase due to climate change. Suitable areas for its cultivation by 2070, areas with high and relatively high priorities will decrease by about 9 percent and areas with relatively low and low priorities will decrease by about 15.5 percent. With the reduction of these middle class areas, the priorities increase by 24.4% compared to the current situation. Scenario RCP 4.5 shows more balanced conditions than RCP 8.5 and moving from optimal conditions by considering adaptation strategies to create conditions without adopting adaptation strategies in saffron cultivation. In the simulation performed in RCP 4.5 scenario, with increasing temperature thresholds in three reproductive, vegetative and stagnation phenological periods of saffron growth, the desired areas from the west and the center of the province in the current situation have been transferred as a strip from north to southeast. In this scenario, the areas with moderate and relatively low priority are more scattered in the west, center and southeast. And low-priority areas are concentrated in the southwest of the province.

The reproductive period of saffron growth in Lorestan province is October. Spatial variations in the priorities of suitable areas for the reproductive period Saffron growth based on the average temperature in the reproductive period in the current situation is 2% of the total area, which is located in the northern and eastern regions of the province. The growing season of saffron in Lorestan province is from November to May. The area of priorities of suitable areas for the growing period of saffron, based on the average temperature index in the current situation, is the highest area with high priority (66%), followed by medium priorities (26%) and low priorities (8%). The period of stagnation of saffron growth in Lorestan province is from April to September. The area of priorities of suitable areas for the period of stagnation of saffron growth based on the average temperature index is the dominant spatial pattern with low priority and its area is 99%. High priority areas with 1% are scattered in the north and east.

One of the most important challenges facing policymakers and urban planners in recent decades is the issue of accessibility to a variety of urban services. The main purpose of this study was thecalculation of the accessibility of census blocks to medical centers using the Two-Step Floating Catchment Area (2SFCA) method in Isfahan City. In the present study, according to the conditions with and without the limitations of the accessibility radii, different types of distance decay functions were used. The results showed that the 2SFCA method with the use of the cumulative opportunity negative linear function had the highest average of correlation for calculating accessibility to medical centers in comparison with other functions. Calculation of average accessibility in the 15 main regions of Isfahan City showed that the central regions (3, 1, and 5) had the highest decrease and the marginal regions (9, 8, and 11) had the highest increase in the unlimited compared to the limited mode. In general, based on the obtained results of 2SFCA method and the calculated Gini index, the level of inequality in accessibility of census blocks to health services was high in Isfahan City and this inequality increased in terms of accessibility to both hospitals and clinics. Since the extended 2SFCA method has a high capability for assessing supply and demand, as well as catchment area, application of this method can provide a great help for managers and planners in theassessment of the population’s access to a variety of services, such as emergency services and healthcare.

Road accidents are killing a large number of citizens every year and it is actually considered one of the serious issues of the country and Lorestan province. The main goal of this research is to analyze the vulnerability of the Khorram Abad-Boroujerd axis road transport network under climate change conditions.

The method used in the present study is double mass test, temporal-spatial analysis using Gis software and forecasting future conditions under the scenario and global climate model in a 30-year statistical period, which temporal and spatial analysis of data has been investigated by weighting method. In addition, linear regression has been used to investigate the relationship between accidents.

It showed that the most effective climatic indicators in this axis, which is the longest and most important axis of Lorestan province, are the maximum daily precipitation in the month and cold days. Also, the trend of indicators in the middle and end parts of this axis is decreasing and the amount of accidents shows the same trend. Unlike the beginning and end areas of the axis, two indicators affecting this axis in the middle areas show an increasing trend. The number of accidents during the statistical period affected by these two indicators is increasing.

The fit of the regression model for the studied axis is significant at the P 0.05 level and the most effective climatic indicators are the maximum one-day rainfall and cold days respectively. In the forecast estimates, the highest amount of accidents in the central parts of the axis is about 5-6% for two RCP scenarios of 4.5 and 8.5. The results of this research have been consistent with the research conducted under the headings related to climate and road accidents by Francois, Stoker et al. and Amlong and Wiener.

The occurrence of any possible change in the future climate will seriously change agricultural production at various levels and will be able to significantly change the crop systems that have evolved under the current climatic conditions. The aim of this study is to investigate the changes in key indicators of agricultural climate based on temperature in the context of climate change. This achieve the aim, the spatio-temporal variations in extreme temperature indices in the current conditions (1986–2016) and under scenarios of RCP4.5 and RCP8.5 (2020–2070) was analyzed based on observed data and climate models in Lorestan province. The agro-climate indices related to temperature are consists of Daily Temperature Range, Frost Days, Days with temperatures below -20 °C, Length of Growing Season and Growing Degree Days. The results indicate that the Daily Temperature Range is approximately -0.4 °C decreasing and 0.3–0.9 °C increasing in the current situation and in the RCP8.5, respectively. Indices of Frost Days and Days with temperatures below -20 °C will decrease by about 20 days in the future compared to the current condition. Compared with the current situation, Length of Growing Season and Growing Degree Days will increase by an average of 60 days a year and by about 300 degrees in the future, respectively. In general, in the future, the warm and cold periods of the year will increase and decrease, respectively. This leads to an increase in evapotranspiration and a decrease in soil moisture and a reason to reduce water storage and ultimately reduce crop yield.

Changes in crop growth at relatively short intervals, asymmetry of cultivation of similar crops, the spectral similarity between different crops at certain times of the growing season, and lack of ground data make classifying crops in satellite imagery a challenging task. Changing the amount of canopy and greenness during the growing season is one of the most prominent characteristics of vegetation, including agricultural products, which can be monitored by using time series of vegetation indices that have useful information about the sequence of phenological features of crops. The use of deep learning methods with the ability of learning sequential information obtained from these time series can be useful in crop mapping and reducing dependence on ground data. The LSTM network is one of the types of RNNs in sequential data analysis that has the ability to learn long-term sequences of time-series information. Therefore, in this study, after extracting the NDVI time-series of 9 different dates from Sentinel-2 satellite images for a region located in Moghan plain, with ground labeled data related to the type of crops cultivated, we trained a convolutional LSTM network. Then we used this trained network to classify agricultural products in another region of the plain as a test site, and achieved an overall accuracy of 82% and a kappa coefficient of 0.8. Increasing the number of ground samples and selecting the exact boundary of crops, can increase the efficiency of the method used.

 To investigate the super-heavy precipitation of Karkheh Basin, daily precipitation statistics of the basin (2005-2018), radar satellite images of Sentinel 1, Landsat, and data on specific humidity, pressure, geopotential height, omega, and wind flow have been used. The results show that the total of heavy rainfall by year shows an increasing trend. Heavy rainfall varies from a minimum of 11 to 40 days (long-term cumulative amount) at the basin level. Most of the heavy rainfall in the basin occurs in the last days of winter and the first days of autumn. The location of Siberian high-pressure, Sudan low pressure, Pakistan low-pressure surface systems and dynamic high-pressure systems of Saudi Arabia and the Mediterranean is the dominant pattern and determinant of extremely precipitation in the Karkheh catchment. The main reason for the increase in the lake's water area in extremely precipitation is not the increase in the number of days of the rainy, but the large expansion south of the Mediterranean trough, eastward movement of the Saudi anticyclone on warm seas and vertical water vapor flux north of the warm southern seas.  Also, in examining the position of the jet core to create extremely rainfall, the most suitable position of the core was observed in the transverse position of 24 degrees north and longitude 42 east (center of Saudi Arabia), so that in this case the most unstable part of the jet (exit area Jet) corresponds exactly to the front of the trough on the Karkheh basin.Highlight The synoptic conditions of super-heavy precipitation are such that in the lower levels of Troposphere, the pattern of Siberian high pressure systems,Sudan low pressure , Saudi Arabia high pressure and the combined model of Sudan low pressure - Pakistan low pressure and in the upper levels of Troposphere.the large expansion south of the Mediterranean trough, eastward movement of the Saudi anticyclone on warm seas and vertical water vapor flux north of the warm southern seas, It plays a major role in the intensity of super-heavy precipitation and increasing the water area of Karkheh Dam Lake.Extended Abstract Introduction Heavy rainfall is when the amount of rain or snow experienced in a place is significantly more than normal. Heavy rainfall is an indicator of climate change and is dependent on climate change, because as temperatures rise, the atmosphere will be able to hold more water vapor, and warmer oceans will increase the amount of water evaporated into the air. Karkheh River is one of the main streams in the southwest of Iran. Due to the geographical location, topography and also its position in relation to the circulating pattern of the atmosphere, the mechanism of precipitation in this basin is very similar to the mechanism of precipitation systems in tropical regions. In most parts of the basin, even in the cold days of the year, rainfall falls in the form of rain. On the other hand, due to the precipitation mechanism of this region, most of the precipitation occurs in the form of short-term rainfall systems and sometimes in the form of small precipitation cores. Due to the hot nature and high humidity potential on the one hand and access to warm seas on the other hand, these systems cause heavy rainfall. Due to the nature (liquid precipitation) and precipitation mechanism of the systems in this basin, there is a possibility of flooding in this The basin is larger than other basins.Methodology Research Method In this research, the environmental method is circulating. First, the daily rainfall statistics of the watershed in an 11-year statistical period (2005-2018) are extracted for all synoptic stations. Then, based on global and national criteria for selecting heavy rainfall and extremely rainfall for the rainfall period of the basin is extracted. In the second step, after determining the number of days of heavy rain, satellite images of Sentinel 1 and Landsat radar in the days before the rain and also after heavy rain were called in the Google Earth engine system. After radiometric and atmospheric corrections of satellite images, NDWI and Log Ratio indices were used to calculate the water area and water changes of Karkheh Dam Lake. Step 3: Atmospheric data for days with extremely using the US National Oceanic and Atmospheric Administration and other related sites in the range of 10 ° E to 80 ° N and 0 to 70 ° N Extracted and plotted with geopotential moisture, omega and moisture, flow and moisture for sea levels, 1000, 850, 700 and 500 hP and jet of 300 and 250 hP in Grads environment. The systems are originated using sea level data and 1000 and 850 hPa. Results and discussionThe highest frequency of heavy rainfall in the basin occurs in autumn and then winter. Heavy rainfall varies from a minimum of 15 to 49 days (cumulative is long-term) at the basin level. In addition to the 30 mm threshold for another day, another condition was considered for selecting extremely rainy days, so that at least 50% of the stations on that day had more than 30 mm of rainfall recorded. Based on the mentioned dual conditions, 7 pervasive events were identified for heavy cloud precipitation in Karkheh basin. In the occurrence of extremely, the area of Karkheh Dam Lake has been increased from a minimum of 5 to a maximum of 51 square kilometers. The main reason for the increase in the lake's water area in extremely precipitation is not the increase in the number of days of the rainy, but the large expansion south of the Mediterranean trough, eastward movement of the Saudi anticyclone on warm seas and vertical water vapor flux north of the warm southern seas.  In all study samples of extremely precipitation, in the lower levels of Troposphere, the pattern of Siberian high pressure systems, Sudan low pressure, Saudi Arabia high pressure and the combined model of Sudan low pressure of Pakistan low pressure and in the upper levels of Troposphere, Saudi Arabia anticyclone and Mediterranean trough, The shape of the trough dominates the area.The transfer moisture of the warm southern seas (Oman Sea, Arabian, Red Sea) from the lower levels and the humidity of the tropical convergence region at the upper levels are the most important causes of atmospheric disturbances and heavy rainfall. ConclusionExtremely precipitation is one of the climatic hazards whose changes in the basin can have irreparable consequences for the region; Direct feedback from extremely precipitation is pervasive floods. The results show that the total of heavy rainfall by year shows an increasing trend. Heavy rainfall varies from a minimum of 15 to 49 days (long-term cumulative amount) at the basin level. Most of the heavy rainfall in the basin occurs in the last days of winter and the first days of autumn. The location of Siberian high-pressure, low-pressure Sudan, low-pressure Pakistan surface systems and dynamic high-pressure systems of Saudi Arabia and the Mediterranean is the dominant pattern and determinant of extremely precipitation in the Karkheh catchment. The main reason for the increase in the lake's water area in extremely precipitation is not the increase in the number of days of the rainy, but the large expansion south of the Mediterranean trough, eastward movement of the Saudi anticyclone on warm seas and vertical water vapor flux north of the warm southern seas.  Also, in examining the position of the jet core to create extremely rainfall, the most suitable position of the core was observed in the transverse position of 24 degrees north and longitude 42 east (center of Saudi Arabia), so that in this case the most unstable part of the jet (exit area Jet) corresponds exactly to the front of the trough on the Karkheh basin.Funding There is no funding support.Authors’ Contribution Authors contributed equally to the conceptualization and writing of the article. All of the authors approved the content of the manuscript and agreed on all aspects of the workConflict of InterestAuthors declared no conflict of interest.AcknowledgmentsWe would like to thank the Meteorological Organization for permission to use the daily Meteorology data.

Qualification and assessment of job competencies of drivers increase productivity and reduce the rate of bus accidents. Among the important benefits of pre-employment qualification is the increased level of job compatibility of urban bus drivers. This paper aims to design a competency model for urban bus drivers in Tehran. The research method is quantitative and applied in terms of purpose. The statistical population includes 2000 private-sector drivers of urban buses in Tehran. The quantitative sampling method is simple random due to the homogeneity of the statistical population. The relevant questionnaire was distributed through probability sampling based on Cochran’s equation among 321 drivers. The results were obtained by analyzing data through confirmatory factor analysis using the factor loading rotated matrix. The weight factors for the the codes were calculated between 0.702 and 0.990 and for the categories between 0.841 and 0.980. The results confirmed all the seven dimensions, which had been obtained previously from the qualitative grounded theory through in-depth and semi-structured interviews with experts. These dimensions include general competencies, physical health, psychological health, driving knowledge, driving skills, communication skills, and cognitive ability.

Urban growth has accelerated in recent decades, therefore, predicting the future growth pattern of the city is very important to prevent some environmental, economic and social problems. The city of Tabriz has also experienced rapid growth of urban lands due to significant demographic changes, which requires accurate simulation of urban growth to prevent negative environmental and economic consequences. The purpose of this study is to evaluate the performance accuracy of the proposed machine learning algorithms by spatial cross-validation method in combination with the cellular automata model to simulate urban growth.

In this study, to analyze urban land use changes, Landsat satellite images related to the years 1997, 2006 and 2015 were classified using the support vector machine algorithm. In the next step, change potential maps of non-urban to urban areas using random forest algorithms, support vector machine and multilayer perceptron neural network for two periods of calibration (1997 and 2006) and validation (2006 and 2015) based on distance from the main roads, distance from the city center, distance from built-up areas, distance from the rivers and railways, as well as slope, elevation and two-class (agricultural / barren) land use layer were produced as effective factors in the growth of the city. Finally, using the cellular automata model, the growth simulation of Tabriz city based on land use and change potential maps obtained from machine learning algorithms for the mentioned periods was performed. To prevent over-fitting of algorithms to training samples and to obtain optimistic results, in the process of extracting optimal parameters of machine learning algorithms, the spatial cross-validation method was used to reduce spatial correlation between training and test data.

The results showed that the random forest algorithm with the area under the ROC curve of 0.9228 compared to the support vector machine and multilayer perceptron neural network algorithms with 0.8951 and 0.8726, respectively, had a better performance in estimating the change potential of non-urban to urban areas. Furthermore, in comparison with others, random forest also clearly showed local variations in potential change. Finally, the growth of Tabriz city was simulated using the cellular automata model based on the obtained change potential maps. Comparison of the prediction map in the validation period with the current situation of urban areas in 2015 showed that the accuracy of urban growth simulation model based on random forest with a Figure of Merit index of 0.3569 compared to models based on support vector machine and artificial neural network was more accurate in allocating non-urban to urban lands with 0.3496 and 0.3434, respectively.

As machine learning algorithms such as artificial neural network, support vector machines and random forest are capable of solving non-linear problems, using them is strongly recommended for urban growth simulation. Also, among the algorithms used in this research, the random forest algorithm based on ensemble learning has a higher advantage than the two support vector machine and the artificial neural network algorithms.

Temperature assessment and forecasting is one of the most practical estimates of climatic elements. Today, the agricultural and industrial sectors are highly dependent on the temperature conditions. Temperature is one of the most important climatic meters that is one of the main factors in the climate identity of each region. The purpose of this study is to make a model for predicting the average monthly seasonal temperature of selected stations in Lorestan province, including Al-Shatrami region. Identification and detection of vulnerabilities in the infrastructure of Aleshtar districts in the conditions of climate change. And due to the inadequacy of the 30-year time series of Al-Ashtarl, neighboring cities such as Khorramabad-Aleshtar-Borujerd synoptic stations have been used, because the artificial neural network method has a great ability to simulate and predict atmospheric elements. And the weather, especially the temperature. To model and predict the seasonal monthly temperature, the r programming tool software of the fOre gast package has been used. Two tests of estimator trend analysis have been used. The 30-year time series trend of these elements was examined during the basic statistical period (1989-2019). The climate cycle was reported and extracted under two scenarios: NNAR and forEgast. The artificial neural network is one of the most powerful models capable of receiving and displaying complex Data input and output is one of the most widely used neural network (NNA) models to determine the best network inputs.

Today, climate change is considered a serious challenge to human societies and the environment and has caused anomalies in the Earth's climate system. According to scientists, an increase in the average global temperature is inevitable. In this study, the climatic elements of temperature and precipitation of Ahar synoptic station for the near future (2026-2045), middle future (2046-2065) and distant future (2066-2085) using the outputs of the CanESM2 climate change model. Based on RCP2.6, RCP4.5 and RCP8.5 with SDSM exponential microscale model and also the annual trend of these changes was investigated using Mann-Kendall test. Based on the output of the model, it was determined that in the near future, precipitation will occur in February and November, and in the next two periods, mid and distant, in October, the greatest decrease will occur, and for April, May and August, the precipitation will increase. On average, the minimum temperature will increase to 0.38 degrees Celsius, the average temperature to 0.52 degrees Celsius and the maximum temperature to 0.82 degrees Celsius. Based on the results of Mann-Kendall test, the annual trend of precipitation in the future will be decreasing, the average temperature in the three scenarios will have an increasing and significant trend and the temperature elements (minimum, medium and maximum temperature) in RCP8.5 will be increasing and significant.

This study was aimed to investigate Precipitation Extremes variability in Bakhtegan Basin. Climate data corresponding to Bakhtegan basin was extracted from AgMERRA dataset for the study time period (1980-2010) using R software. Daily precipitation data was also extracted from the meteorological stations data arhive in the basin during the study period. Rx1day, Rx5day, PRCPTOT, CDD, R10mm, R20mm, R95p and R99p indices were selected and calculated to study climate change in the region. RX1day results with the changes trend of -1.186 to 0.217, RX5day, -0.624 to 0.82, R10, -0.179 to 0.025, R20, -0.06 to 0.046 and PRCPTOT, -3.675 to 2.028 showed a decreasing trend in catchment’s most parts and jointly in the catchment’s southern and western parts. Decreasing precipitation, the consecutive dry days (CDD) increased and generally showed an increase throughout the basin (except for a small section in the catchment’s centeral and eastern parts). The most consecutive dry days were observed 259 days in 2008, 245 in 1983 and 264 in 1999 in Shiraz, Doroudzan Dam and Aliabad Kamin stations, respectively. Both R95p and R99p indices illustrated increasing and decreasing trends in different parts of the basin.

Drivers’ qualification is the main prerequisite for improving productivity and safety of the public fleet of urban buses. This study aims to present a comprehensive and indigenous model for the qualification of Tehran urban bus drivers.

The grounded theory qualitative research method was used in this paper. This method is used for research topics with little previously established literature. There has been no previous indigenous study for the comprehensive qualification of Tehran bus drivers using qualitative research methods. The results can be considered as the cornerstone of domestic qualification of urban bus drivers. In-depth semi-structured interviews were used for data collection. Initially, 15 experts were selected to be interviewed. They had academic and professional backgrounds in diverse transportation disciplines. The homogeneous non-probability purposive sampling method was used to select the experts. The responses of only 10 experts were used as theoretical saturation was attained.

The codes derived from qualitative interviews resulted in an indigenous qualification model of bus drivers. In the open coding, axial coding, and selective coding stages, 77, 23, and 17 codes were extracted, respectively. The validity and reliability of the research were confirmed statistically.

A conceptual framework was designed for the qualification of urban bus drivers based on a model with seven dimensions including general qualification criteria, physical health, psychological health, driving knowledge, driving skills, communication skills, and cognitive capacity.