جستجوی مقالات مرتبط با کلیدواژه « Forecasting » در نشریات گروه « جغرافیا »

To conduct this research, data on monthly synoptic and hydrometric precipitation observations from the National Meteorological Organization and the Ministry of Energy were obtained for a 30-year period (1976-2005). To assess future changes in rainfall, historical data from the period (1976-2005) and simulated climate data from the period (2021-2050) using two models (CM3 and CSIRO-Mk3.6) from the CMIP5 series were used. These simulations were based on four scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) with a spatial resolution of 0.5 x 0.5 using the BCSD method. A mean-based (MB) strategy was employed to correct any bias in the model outputs.  The results of the AOGCM models indicated that the CSIRO-Mk3.6 model had a lower error coefficient than the GFDL-CM3 model when simulating precipitation in the Large Karoun case. The average future rainfall (2021-2050) across the entire basin, compared to the average observed rainfall during the statistical period of 1976-2005, exhibited a significant decrease in both the amount and extent of precipitation in both basins for all models and scenarios. In the Great Karoun Basin, heavy rains were consistently concentrated east of the basin across all scenarios and models, with the central foothills experiencing the highest rainfall and the southwest and southeast regions receiving the lowest amounts.  The findings of this study estimate rainfall to range between 83-116 mm, with the highest rainfall expected in the Greater Karoun Basin under the rcp4.5 and rcp2.6 scenarios for both models.

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

Projection of summer monsoon rains in Southeast Iran based on Ensemble model the precipitation factor has a variable and random nature and has a different behavior in terms of space and time. Therefore, the prediction of precipitation has more uncertainty compared to other meteorological variables. In the current study, the data output of Cordex database and CMIP5 models were used using the neural network method to reduce the uncertainty and estimate precipitation properly. The results showed that due to the high correlation of temperature, humidity and air pressure with precipitation, the use of these variables is beneficial in reducing the uncertainty of precipitation forecast. In addition, the non-linear method of artificial neural networks can be used to bias the rainfall data of Cordex database and CMIP5 to forecast the rainfall in the southeast of the country. Another result of this research is the increasing trend of rainfall in the southeast of Iran, especially in the coastal areas. This can be considered as a result of the increase in the level under the influence of rains affected or simultaneous with the southwest monsoon of India. The increasing trend of precipitation in the southern coasts is also related to the increase in the storage capacity of moisture content. The interannual variability of India's monsoon rainfall also shows a steady positive trend under continued global warming. Since both the increase in the duration of monsoon rains and the increase in interannual variability in the future are seen in most models, we can be confident in these predicted trends. Indian summer monsoon rainfall is also predicted to be higher under global warming in the 2050s compared to the baseline.

Urban expansion has led to the formation of complex forms of the spatial existence of cities. The consequences of this expansion have been shown in the form of agricultural land destruction, environmental damage, and uneven and scattered urban growth. In recent years, several programs have been prepared to organize, manage and guide the expansion of the city, which due to the lack of sufficient knowledge of the influencing and driving factors of the development, and the state and manner of the future expansion of the city, make decision-making in this field a serious challenge. As a result of the increasing growth of cities, the physical expansion of cities to the surrounding areas as well as the increa se of density and accumulation within the cities will be inevitable hence the peri-urban spaces are expanding When the population increases and urbanization accelerates, new residential spaces are created in the periphery of cities from the "city-rural" confrontation, which is strongly influenced by urban spaces. Such residential spaces are called "Pirashahr". This urban growth will bring serious and countless problems. Since the instability of the development of human societies in the last two centuries (after the industrial revolution) and its harmful consequences, which are a function of population variables, per capita and consumption patterns, attention to the principle of sustainability is being questioned more and more Table 2 shows the results of the accuracy evaluation of the produced maps. According to this table, four parameters are presented for overall accuracy, kappa coefficient, producer accuracy and consumer accuracy. By examining the obtained statistical results, we find that the accuracy and parameters are often above 90% (except for the kappa coefficient of 2000 and the forecast kappa coefficient of 2020), which is very appropriate and in several cases these statistics are close to 100%. The results of this research are also in line. The results of their research show that vegetation lands have declined sharply in the time interval. Also, based on the results of the sacrificing and colleagues (2014), farmers' land has increased and urban growth has increased the study of the study, and the results of the overwhelming land change means also indicated the continuation of this process. Research Results Majid (1393) in evaluating changes in land margin in Urmia during the years 1989 to 2013, and then predicting the trend of changes by 2035 of the combination of Markov chain and automated cells showed that the growth of Urmia city has always caused the destruction of agricultural lands and gardens of this city and becoming residential lands. Ali Mohammadi et al The population growth of the city and followed by the change in land cover and destruction of vegetation. Growth of urban population and a tongue to build a building on the populations of the crowd; During the destruction of crops, ranges, green space, the field of changes and significant changes in land cover, as well as the change in the climate. This research, using the modeling and analyzing chain of the Markov and automated cells, was modeled and analyzed and analyzed the changes coated on the city of Bandar Abbas and its penetration area. For this purpose, the first Latest satellite imagery was classified for 2000, 2005 and 2010, 2015 and 2020 with the most similar similarity approach. The classifications were analyzed and the results showed a coaxial coefficient of 90% and the high accuracy of the classification. In the following, changes in land coverings were carried out during the period (2000-2005), (2005 to 2010) and (2010 to 2015) (2015 to 2020) and (2015 to 2020). In the next step, using the Markov chain analysis and automated cells, modeling and predicting changes in landing coating in the future were taken. , the results showed that the increase in significant percentage of changes in the water, which indicates that this year increased by the year in 2015 by 2020. In general, the area of land is provided to the research below to be reviewed in the following charts. The vegetation was reduced from 4707 hectares in 2000 to 3518 hectares in 2005 and 3321 in 2010 and 2693 in 2020. According to the model, this decline will continue to be a downtrend, as it will reach 1984 hectares in 2025. The city and the built-in land has been ascending since 2000, as of 5879 hectares in 2000 with about 582 hectares reached 6461 hectares in 2005. This land ups will be continued, and the prediction map shows that these land will increase to 7976 hectares in 2025. According to the findings obtained from the results of the random forest model, the highest parameter influenced in the development of the city was the main way of the index. The results of this research and using the processing of moonlight images showed that the Bangea Abbas city city during the 20 years (2000 to 2020) were changed over the range of farmers and the city center. Study of land use maps in this study showed that the trends and development of the city of Bandar Abbas did not comply with a uniform trend and in terms of changes in fluctuations. The city's growth is out of range, in the southern part, given that there is a major range as a sea that is the ability to expand in these areas very low; However, as the city's growth in the southern margin, due to government decisions for tourism growth as well as marine trade, has led to the expansion of human structures in this area. On the other hand, in the northern areas of Bandar Abbas, due to the possibility of developing the city, during the study period, it has been continuously taken to these populations. A point of view can be concluded that the land-related activities of the land market have made a large growth in the north of the city, while the region is in line with the shape of 17, the proportion of these areas for urban development is weak.

Natural parameters are one of the main determinants of the physical development of cities and settlements. In a mountainous area, the effects of these factors have become a barrier to development and can have natural hazards. In this research, it is tried to identify the optimal directions of physical development of the city of Nurabad as a relatively high region by identifying its effective factors and evaluating it. To achieve this, seven effective indicators (elevation, gradient, gradient direction, lithology, distance from the fault, distance from the waterway) were used and to assess, model, and predict areas suitable for physical development of the city from Landsat satellite imagery and Models of FUZZY-AHP and Makov and Markov's predictions have been used. So that each of the layers is fuzzy according to the fuzzy membership functions in GIS Arc 10.3 software. An analytical comparison on the appropriate areas of the city based on the critical points with the appropriate zones. Finally, the final map with the two models was classified into five classes. The results of the research showed that up to 1404 horizons of the city were developed eastwards in Although this pathway is not a suitable route, due to the existence of the main Kazeroun fault and the main waterway, the most important risk factors in the city are considered to be the best place for the development of the city of the western and southwestern regions of the region, which is 13% of the area of ​​the basin Includes.

Heat waves are one of the most important weather disasters, and their destructive environmental effects require knowledge and forecast. The purpose of this study is to forecast the future conditions of heat waves in Ardabil, Tabriz and Urmia during the years 2020-2050 using SDSM downscaler and Baldy index. For this purpose, the maximum daily temperature data of selected cities from 1961 to 2005 after preparation were entered into SDSM software and analyzed with CanESM2 model with 2٫6, 8٫5and RCP 4٫8 scenarios and with the output of the model, the maximum temperature trend of the three stations during the next 30 years was investigated and their heat waves were Forecasted using the Baldy index. According to the results of the error measurement of the model, the simulations performed had acceptable accuracy. Due to the fact that each of the cities showed the least error in one scenario, so the heat wave prediction of each station was done using the least error scenario. Based on the results the average maximum temperature will decrease from late winter to late spring compared to the base period and will increase slightly in mid-summer. In general, we will see a decrease in the average maximum temperature in Ardabil and an increasing trend of the mentioned parameter in Tabriz and Urmia cities during the years 2020-2050. Also, the study of the predicted heat waves in the next thirty years showed that one-day waves in Ardabil and Urmia will be a slight increase and in Tabriz will be almost without trend. Two-day heat waves will decrease in Tabriz and Urmia and will increase in Ardabil. The trend of three-day heat waves in Ardabil and Urmia will be decreasing and increasing in Tabrizthe highest continuity of heat waves was four days, which was forecast in Urmia. The highest frequency of two- and three-day heat waves in all three cities will be in the cold seasons of the year.

One of the greatest human problems in the present and future is the scarcity of water resources and the inadequate distribution of water resources. One of the important climatic elements and parameters in each region is the prediction and estimation of precipitation and temperature, and this helps planners to achieve good harvest patterns from water sources and to farmers for crop management, especially dry farming. In addition, it reduces the likelihood of irreparable damage caused by droughts and floods. Precipitation is an important source of water throughout the world, including arid and semi-arid regions. The element of precipitation is one of the most complex natural phenomena. In fact, the tangible and intangible effects are so scary that they move from an explicit legal system to a complex structure. Another important factor affecting water resources is the temperature element. Therefore, investigation and prediction of temperature in each region and catchment can be of great importance for optimal utilization of water resources, reduction of evapotranspiration, etc.The purpose of this study is to predict the precipitation and temperature in Sistan and Baluchestan province. Due to climate change and the downward trend of precipitation and increase in temperature in the region, it is attempted to predict precipitation and temperature in 13 Next year, better management of the use of water resources in the agricultural and non-agricultural sectors.

General pattern of non-seasonal and seasonal order A) Degree seasonal autoregressive process The general model of non-seasonal and seasonal order is shown in relation (1) The seasonal autoregressive pattern can be regarded as a special case of the ordinary non-seasonal autoregressive model in which the coefficients are only zero at the seasonal delays . (B) The process of the seasonally moving average of the degree Q of this process is expressed in (2):

In the present study, the Akaike criterion based on the residuals of the fitted model and the best fitted model criterion were selected from all the models for the Hirmand catchment. According to the criteria, the best model is the one that has the least value P- Value, and the highest T. The results of these criteria were identified. The SARIMA model has the least AIC and SBC value. Results of different SARIMA models of time series and criteria,P- Value, AIC ,SBC and T. Table 2 shows the catchment rainfall. For the catchment only in the model , the absolute value T of all parameters except the constant is greater than 2 and it is less than 0.05 and also has the lowest criteria AIC and SBC values, so the best model is known.Table (4) shows the comparison of different time series models for the static temperature data of the Hirmand catchment over the 18-year period (2001-2019). According to the criteria P-Value, ، ، and . the best model is the one that has the least value, and the highest. The results of these criteria were identified. The model has the least value and. Results of different models of time series and criteria ، ، and . Table (4) shows the catchment temperature. For the catchment temperature only in the model the absolute value of all parameters except the constant is more than 2 and it is less than 0.05 and also has the lowest values of criteria and, therefore, the best model is known.

In this study, the results of Lajang-Box test for the selected model of monthly temperature and precipitation of the Hirmand catchment for all delays were P-VALUE greater than 5%, which confirms the hypothesis that the residuals are uncorrelated. Also with varying degrees of freedom the test statistic is less than 5% of the kiddo table and the null hypothesis is accepted. The monthly temperature of the Hirmand basin has an increasing trend and the monthly rainfall has a decreasing trend, so planners and policymakers should apply policies to optimize water use in the region. 1- Optimal use of water resources is recommended in case of increase of temperature and decrease of bonding in the area. 2- Manage irrigation water during drought and allocate water to more essential crops and crops that have a comparative advantage. Applying the knowledge of experts in agriculture, plant breeding and climatology to adapt the type and extent of crop cultivation to climatic conditions.

In recent decades, rising global temperatures have upset the climate balance of planet and caused widespread climate change in most parts of the world, known as climate change Index. In the present study, climatic variables in six synoptic stations in the southeastern part of Iran (Zabol, Zahedan, Khash, Iranshahr, Saravan and Chabahar) from 1987 to 2019 were studied in two time periods (2021-2040 and 2041-2060), considering the uncertainty of the general circulation model (Hadcm2) was investigated and analyzed. After reviewing and evaluating the model, the values ​​of climatic variables in two scenarios, RCP2.6 and RCP8.5, were scaled in the studied periods. The results of LARS-WG model accuracy evaluation in simulation of climatic variables based on MAD, RMSE, MSE, MAPE indices in the southeastern part of Iran in the validation stage showed that there was a large correlation between the simulated values ​​and the base period. Based on the output results of LARS-WG model, it shows the increase of temperature in all stations in the two studied periods. The rate of temperature increase in coastal areas is less than other land areas. Temperature changes fluctuate uniformly and precipitation changes fluctuate at all stations. The amount of rainfall during the winter and spring seasons in all stations is increasing; Future climatic conditions such as number of frosty days, number of hot, dry and wet days were calculated and the results showed that in the next period, the number of hot days, the number of dry days will increase compared to the base period and the number of frosty days will decrease. If we do not adhere to the reduction of greenhouse gases, the increase of climatic variables, the minimum temperature and the maximum temperature and the decrease of precipitation will increase in the 2041-2060 decade. Monthly forecasts for future periods in the mentioned scenarios also indicate that the warmest month in the province is on average in summer (July), the coldest month in winter (December and January), the most precipitation in winter and spring (December, January and March) and evapotranspiration peaks in summer. Finally, the results show the trend of increasing temperature and decreasing rainfall in the coming decades in the southeastern regions of Iran.

Urban prosperity is a concept related to balanced development and harmonized in the environment is committed to justice and embodies human activity as a kind of social construction. Nowadays a different view of the future has caused man not to seek the future, but to build his desired future by using various tools. On the other hand, the challenge of new urban life, environmental and social problems lead to future research approach in urban planning and using various devices to build a favorable future. The present study uses the technique of cross-impact analysis, which is one of the common methods in futurism by using MicMac software, which it has analyzed the components of urban prosperity in Ahvaz metropolis. In the following using the Delphi method which 30 components in five areas such as (productivity, infrastructure, quality of life, equality and social participation, and environmental sustainability) extracted as indicators of urban prosperity. The results indicate that there are five categories in the scattering pages like (influencing factors, bilateral factors, regulatory factors, influential factors and independent factors) are identifiable. Finally among the 30 mentioned factors after examining the effectiveness of these factors on each other and on the future situation of the metropolis of Ahvaz by direct and indirect methods, 7 key factors in (literacy rate, slum families, air pollution, unemployment rate, cultural centers, life expectancy at birth and poverty rates) have been selected to play the crucial role in the future situation of urban prosperity in Ahvaz. The results show that none of the components of urban prosperity in Ahvaz can be defined by experts as a target factor. This issue indicates the multifaceted issue of prosperity of Ahvaz metropolis.

Tourism is one of the most important development tools in the world. Golestan province based on natural, historical and cultural resources The region is prone to the development of the tourism industry And This province should be in its future policies and plans Pay particular attention to tourism as an effective tool for continuing the process of political, cultural and economic development. Therefore, this research is aimed at developing scenarios for developing the tourism area of Golestan province with a future study approach. In terms of the purpose of this research, the method is a combination of documentary and scrolling methods. The statistical population of this research includes all experts.Which has enough knowledge and experience in the tourism sector. The non-probabilistic sampling method is using two methods for judging and snowball sampling. The data gathering tool was a questionnaire and an interview with experts. The results of this study Based on the Mic-Mac software output showed that the eight main factors affecting the future development of the Golestan province are influential. According to these, eight factors include private sector participation, tourism acceptance culture, entrepreneurship in the tourism sector, establishment of shopping centers, security of domestic and foreign tourists, incomes of the tourism sector and infrastructure of the government in the tourism industry. four scenarios were identified and selected Based on the twofold "uncertainty" and "importance," For the four likely future of Golestan province from the combination of two axes of uncertainty "tourism entrepreneurship" and "tourism acceptance culture".

One of the most prominent features of the historical core of cities is the presence of precious physical elements and textures.With the global registration of Yazd's historical context in UNESCO and its emergence in tourism, the preservation of this texture and its sustainable development are more felt. The Forecasting approach allows for the best possible use of future opportunities and to avoid negative consequences as far as possible by identifying the future opportunities and threats of urban contexts. The purpose of the present study is to identify the key variables are effective in resolving the existing problems in the historical context of Yazd and to investigate the relationships between the variables and how they affect each other.The method of this study is descriptive-analytical.The nature of the data is qualitative and the method of data collection is library, documentary and survey.In this regard, the opinions of 15 experts in the historical context of Yazd city have been used to identify key factors. In the next step, the forces driving the development of the Sheshbadgir district are identified and these factors are prioritized based on the degree of uncertainty and then the scenarios are formed. Accordingly,the most important priorities are to improve the state of exhaustion,reduce crowding and improve the safety of the Sheshbadgir district.The solutions are suggested such as government loans for refurbishment, refurbishment and renovation of residential units, refurbishment and design of sidewalks,raising public awareness of the historical context,providing more facilities and a favorable environment for investment by tourism companies.

The main purpose of this study is to identify and analyze key factors affecting the spatial development of cities in Guilan province. In this regard, this research has been carried out during the following steps. Questionnaire and Delphi technique were used for data collection. In the first step, 60 factors affecting the spatial development of Guilan province were identified based on documentation studies in three dimensions of population, activity and space. In the second stage, the factors affecting the spatial development of Guilan province were extracted through the Delphi method and based on the experts' point of view. A questionnaire was designed and provided to the experts. In this questionnaire, experts were asked to rate each factor in the Likert scale. Finally, after collecting the questionnaires, the mean score for each factor was determined and finally 28 factors from three dimensions (population, activity, and space) were selected for analysis in the Mac software. Finally, based on the Interaction Impact Analysis method, there are 5 key factors including knowledge-based activities, specialty settlements for deploying high technology industries, rail transport, maritime transport capacity, and diverse modes of integrated transport (Rail- Water - Road - Air) was introduced for spatial balanced development in Guilan province

The results of model evaluation during training and testing demonstrated significant accuracy differences between various models. Results showed that in the ANFIS, minimum of R2 in SPI-3 was 0.59, in hyper-humid climates (Ramsar and Bandar-e-Anzali) and maximum of R2 in SPI-3 was 0.78, in hyper-arid climate (Zahedan) and humid climate (Yasuj). Also minimum of R2 in SPI-6 was 0.75, in semi-arid climate (Hamedan) and maximum of R2 in SPI-6 was 0.87, in hyper-arid and arid climates (Zahedan and Mashhad). In SPI-12, minimum of R2 was 0.88, in hype-arid and semi-arid climates (Zahedan and Hamedan) and minimum of R2 was 0.97 in arid climates (Mashhad). Also, results of ANFIS showed that membership functions type and climates type don't have effect on ANFIS performance and when model is using precipitation in two delay step and SPI in 3 delay step, it has acceptable and high accuracy results. In the GMDH, R2 is between 0.91-0.99 in all three SPI scales (SPI-3, SPI-6 and SPI-12) and in all climates which it indicates the acceptable accuracy of this model. In order to evaluate the results of GMDH models, the best models related to M4 and M9 that input variables are {SPI(t-1), SPI(t-2), SPI(t-3), SPI(t-4), SPI(t-5)} and {SPI(t-1), SPI(t-2), SPI(t-3), SPI(t-4), SPI(t-5), P(t-1), P(t-2)}. RMSE values indicated that it increases when climate type is changing. Hyper-humid and humid climates have RMSE more than other climates. It related to precipitation effect in models performance. M5 and M6 models that use just precipitation in the previous months have low performance in drought forecasting. Also results indicate that SPI is appropriate for 12-month scale. In fact, the performance of the models has direct relationship with the increasing of the SPI time scale. Finally, The results of the comparison of observed and calculated values of three SPI scales (SPI-3, SPI-6 and SPI-12) using the GMDH model in all climates showed that drought forecasting is reliable when this method used and it'll use possibility for future drought forecasting. In general, the results are accurate when using ANFIS and GMDH but the performance of the GMDH model is better than other model. Also, execution speed and GMDH calculations are far more than the ANFIS. Finally, in this study, GMDH propose as the best model for drought forecasting

In this research, applied synoptic model for determining the average daily temperature and its relationship with the Geopotential Height in middle level (500 HPa). Therefore, two database were used: database of atmospheric circulations, includes the data of geopotential height at 500 HPa and its data were extracted from the NCEP/DOE(US National Oceanic and Atmospheric Administration) in hours 00:00; 03:00; 06:00; 09:00; 12:00; 15:00; 18:00; and 21:00 in Zulu and other, database of environmental (surface) events. Contain of average daily temperature in the Mashhad, Torbat-Heydarieh and Sabzevar stations in Khorasan Razavi Province. The maximum and minimum of these stations in the time interval from 01/01/1987 to 01/01/2014 equal as 9862 days from the meteorological organization of Iran. Then, was calculated the correlation of the average daily temperature of selected stations with high atmospheric data (500 HPa level) with the northern hemisphere in Surfer Software. The result shown, four regions in the northern hemisphere which had high correlations with selected stations. The correlation results suggest that the United States has 25 pixels, Northern China 25 pixels, Africa 45 pixels and Japan with 65 pixels. Then, weighted average of pixels in heights by multiple regression equation station. The results of diagnostic models indicate that, per geopotential height increase in the profile, the average daily temperatures of selected stations in the Sabzevar 1.4, Torbat-Heydarieh 1.3 and Mashhad 1.3 degrees Celsius will increase.

The purpose of this study was to extract and explore land use changes from satellite images with object-oriented and knowledge-based models, as well as predict these changes using the Markov chain model by the 2030 in the Zailbirchai basin. Considering that the separation of some LULC from each other, such as irrigated wheat, Rain-fed Agriculture, Irrigated agriculture, and orchard in images with a spatial resolution, such as Landsat images and known pixel-based methods, is rigorous. Therefore, in the present study, a method based on the knowledge based was used for the extraction of the LULC with the OLI image of 2015 and its validation was done with kappa coefficient (0.86). Then the result of method was used in the TM image of 1987 (kappa coefficient equal to 0.83). Finally, after the analysis of the changes occurred, temporal changes was predicted for 2030. The classification of images shows the growth of residential, orchard and irrigated agriculture land uses, respectively, 15, 42 and 50 km2 over the course of 28 years. This growth reflects the high consumption of water in the region. Cross Tab, on the other hand, shows the conversion of low-consumption applications such as irrigated wheat to arable crops. Also, in the next 15 years, based on pattern of 28-year-old using Markov chain analysis model, this growth will be up to 8, 27 and 8 km2, respectively.

In recent years, the growth of urbanization in Iran and the increase of migration to the major cities have led to the sudden and abnormal expansion of these cities, degradation of fertile lands and natural resources, and irreparable damages to the nature. As the population of the city of Shushtar has increased, there has been a lot of growth in the built lands in the region, causing a large change in the use of the lands around the city and the degradation of the fertile lands in the suburbs; so that, the continuation of this process could cause irreparable damages to the environmental resources of the region. Land-use prediction models are essential in planning for sustainable use of the lands (Kamusoko et al., 2009: 435, Mas et al., 2004: 94, Sohl and Claggett, 2013: 235). In addition, predicting land use changes and creating a relation between these changes and their socio-economic consequences is very important for sustainable land management (Whitford et al., 2008: 340). So far, the Markov-genetic model has been used in
several studies. Wu et al. (2006) studied the monitoring and forecasting of the Beijing region of China over a 16-year period and used the Markov chain model and regression to predict the land use. Therefore, the purpose of this study was to investigate the trend of land use changes over the past years and predicting the land use and land use changes
using the Markov chain model in the city of Shushtar in Khuzestan province. By predicting land use variations, the development and degradation of the resources can be identified and it can be led to managing the changes in the appropriate pathways (Brown et al. 2000: 247, Hathout, 2002: 229 and Jenerette et al., 2001).
The study area of this research is Shushtar city with an area of 340645.2 hectares located in the North of Khuzestan province. The software packages used in this research include ArcGIS 10.2, ENVI 4.8 and IDRISI Selva 17.0. The images used to extract ground cover classes include Landsat series satellite images; these images were used in this
research due to having a long time series, having an appropriate spatiotemporal resolution to study the land cover changes, and being free. Regarding the existing land uses in the region, the research objectives, and the capabilities of the images used to extract useful information, especially the land use mapping, four land uses including rangeland,
irrigated agricultural lands, rainfed agricultural lands and residential lands were considered. In the analysis of the Markov chain, the cover classes are used as the states of the chain. To determine the possibility of a change, the chain needs two land use maps (model inputs), which are usually obtained by processing the satellite images (Mitsova et al., 2011: 141). Markov chain analysis was performed using Markov chain order in the Idrisi Selva software. Markov chain analysis is provided for two purposes, the first matrix is used for calibration and the second one is used to simulate the possible changes occurring in the future. The output of the model also includes the possibility of transforming the state, transition area matrix for each class, and at the end of the conditional probability images for converting different uses (Gilks et al., 1996: 19 and Weng, 2002: 273).
Regarding the trend of changes during these three periods, the irrigated and residential lands classes had an increasing state, but on the contrary, rainfed lands and rangelands classes had been decreasing. The accuracy of classifications is generally more than 77%, and suitable for use in the Markov model. The results of the detection of changes in 2030 are such that if the current trend continues in the region, 20.33% will be added to the area of the irrigated agricultural land use, so that irrigated agricultural land use constitutes 60.95% of the area in 2030. This increase is due to the changes of the land uses of rangeland and rainfed to the irrigated agriculture. The decrease in the rangeland and rainfed classes will be 21.12% and 0/21% respectively which will be added to the area of the irrigated agricultural lands. These changes are more pronounced around the rural areas in the region.
During the research period, irrigated agriculture has been the most dynamic land use in the region. The area of these lands has increased from 1989 to 2015, so that, 1350131.69 ha has been added to the area of this land use during the three study periods. In the first period, the annual rate of increase was 3650 hectares and in the second period the annual rate increase was 3998 hectares. Considering the lack of change in regional governance and planning, the trend is such, that more than 60 percent of the plain area will be covered by this class in 2030 which can be led to changes in the ecosystem conditions. This result is consistent with the results of Gholamali Fard et al. (2014) in the middle coasts of Bushehr province and is not consistent with the results of Ali Mohammadi et al (2010), Dejkam et al. (2015), and
Ramezani and Jafari (2014).
In general, the results of this study indicate an increase in the area of irrigated agriculture, as well as development of the Shushtar, which has occurred through the disappearance of rangelands and rainfed lands. As it is well known, if the current strategy of land use in this area continues to reduce natural lands and increase urban lands, regardless of sustainable development considerations until 2030, significant environmental problems, including degradation of rangeland, decline in production of the major agricultural products of the region, decrease in the fertility, and increase in the deserts, will be a serious threat to the future ecosystem of the region. Also, considering the current productivity status, the region's economy which is based on the agricultural and livestock production will face a serious threat in 2030. Therefore, this research recommends the use of resulting maps to identify the sensitive areas for better planning
and management of the executive organizations.

The increase of earth temperature causes anomalies in the earth climate that this affair is itself affective on all the aspects of human life. In this context, the aim of this study was to consider the temperature changes and predict the extreme temperatures in Guilan and Mazandaran Provinces. The SDSM statistical and dynamical model is used and also for analyze the temperature changes and trends has been used the Mann-Kendal graphical and statistical technique. In this regard, the daily data of Rasht, Ramsar and Babolsar stations temperature were produced during the statistical period of 1961 to 2010 from Meteorological Organization and also the general circulation models data of HadCM3 and NCEP were taken of related database.The results revealed a significant positive trend in monthly and annual minimum and maximum temperature in all three stations in the first and third priod. Also in the second period, there is not a significant trend in extreme temperatures in Ramsar and maximum temperature in Rasht. The Mann-Kendal graphical test was done on the yearly extent temperatures of 3 periods and its results showed that by not reminding the critical border of 1/96 in the level of 95 percent assurance of changes of increase recess kind and also sudden changes has been occurred in all stations. In the forecast period, is seen the highest of changes in monthly and annual of the minimum temperature.The increase of extreme temperature was predicted about 0/1 to 1/7 centigrade. The short time oscillations and meaningful positive trend happenings in both of the maximum and minimum temperature shows the temperature increase in the future years that this indicates occurred climate changes.

In this study, in order to know the trend of frost phenomenon in Kashan station in the next years, the ice days of this station were modeled. To this end, the comparison and selection of the best fitted model has been investigated by atmospheric circulation models. After collecting daily data of Kashan synoptic station in the period of 1973-2000, according to the data of the general circulation model in the period of 2011-2030 and the selection of the A1 emission scenario, among the general atmospheric circulation models, five applicable models including BCM2, HADCM3, IPCM4, GIAOM, and HADGEM were selected and the climatic data of the station were redistributed using LARS-WG software. In order to create daily data by 2030, the mean and standard deviation of each model were compared with the calculation of bias and absolute error. Considering the lowest absolute error among models, the GIAOM model was selected to predict the minimum and maximum temperature and artificial ice data, as well as to investigate Kashan frost for future climatic intervals. The results of the GIAOM model showed that the highest increase in the minimum and maximum mean temperatures in the future will occur in the summer and autumn seasons and the lowest temperature increase will be in the winter and spring seasons, which indicates that we should be waiting for warmer summers and autumns. The results of frost trends show that the number of days with weak, moderate, and severe frost in the future will be reduced. Meanwhile, the maximum reduction in the number of freezing days will be due to weak freezing. The decrease in the number of freezing days in Kashan station indicates the climatic susceptibility of this region to the global warming consequences and, in fact, shows the sensitivity of the number of freezing days in this region to this phenomenon.

For Iranian citizens traveling to turkey without visa is possible and the main reasons of Iranian citizens for traveling to Turkey are; resting, leisure time, enjoying various attractions like sea side, cultural, trading especially buying clothes and etc. The tourism industry is one of the main driving forces of Turkish economy. The increase and decrease of traveler to turkey depends on various factors in both national and international levels. This article wants to forecast the amount of Iranian tourist which traveling to Turkey by using the Artificial Neural Network; (ANN). On the other hand, the present article attempts to attend to explore the importance of used variables in tourism demand to Turkey. The variables effects on Iranians’ tourism demand were extracted by exploring the literature review, and the present research just utilized 13 variables for forecasting the tourism demand. The results of the research showed that accessed estimates had less error in comparison with real data. According to present research, four important variables effect on tourism demand of Iranian citizens are the daily crude oil producing in Iran, inflation volume in Iran, gross domestic product in Turkey, gross domestic product for working people in Iran.