نشریه پژوهش های اقلیم شناسی
پیاپی 55 (پاییز 1402)

One of the patterns of major climate changes in tropical maritime regions is the Maden-Julian oscillation, which affects sub-seasonal time periods of 30 to 60 days, tropical and subtropical regions. This phenomenon brings the variability of various quantities of the atmosphere and the ocean, such as pressure, surface temperature, and the rate of evaporation from the ocean surface in tropical regions. In this research, first, the daily rainfall data of 48 synoptic stations, 1980-2020, was obtained from the Meteorological Organization. And quality control in three zero levels 1 and 2, including structural quality control, physical limits, correlation of parameters within the report, climatic range, temporal and spatial correlation, quality control.The Madden-Julian Oscillation is one of the large-scale climate change patterns in the maritime tropics, with subseasonal time periods of 30 to 60 days affecting tropical and subtropical regions. This phenomenon can cause changes in various quantities of the atmosphere and ocean, such as pressure, sea surface temperature, and the rate of evaporation from the ocean surface in tropical regions.In this research, the effects of Maden Julian fluctuation on the weather elements of Iran have been investigated with the aim of knowing the effects of different phases in order to improve the quality of forecasts and benefits in territorial planning. At first, the daily rainfall data of 1980-2020 was received from the National Meteorological Organization and quality controlled. Using the Wheeler and Hendon method, the two main components RMM1 and RMM2 were analyzed, based on which the amplitude of the above two components is considered as the main indicator of the intensity and weakness of this fluctuation. This index is based on the experimental orthogonal functions of the meteorological fields, including the average wind levels of 850 and 200 hectopascals and outgoing long wave radiation (OLR) between the latitudes of 20 degrees south and 20 degrees north. The clustering of the 7-day sequence with a component above 1 was used as the basis for clustering all eight phases, and by calculating the abnormality of each phase compared to its long term in the DJF time frame, the zoning of each phase was produced separately. In the end, phases 1, 2, 7, 8 were concluded as effective phases in Iran's rainfall and phases 3, 4, 5, 6 as suppressive phases of Iran's rainfall.Phases with a sequence of 7 days and a component above 1 were used as the basis for clustering all eight phases, and by calculating the abnormality of each phase compared to its long duration in the DJF time frame and by passing the data through the 30-60 day intermediate filter, zoning Each phase was produced in latitude 25 to 40 degrees north and longitude 44 to 63 east. With the investigations carried out and the output of the models and finally the production of zoning related to Iran's rainfall anomalies in the eight phases of the MJO, the country's daily rainfall data were analyzed in the period from 1980 to 2021, which finally after applying Data optimization filters, Iran's precipitation zoning output using Arc Gis software were produced separately for all eight phases. According to the outputs, the distribution of rainfall in the conditions of phase one can be seen with a variety of fluctuations, among which the share of rainfall and rainfall anomalies is greater in the southwestern and southern regions of Iran, especially in the regions of Fars, Hormozgan, West Kerman and Bushehr provinces. It is significantly different from other regions of Iran. In the region of the Caspian coast, the main reason for the rainfall is different from the rainfall in other regions of Iran, and it is not discussed in this analysis. Iran's rainfall anomaly in phase 2 has weakened compared to phase 1 of rainfall, and it can be felt that the anomaly is weaker in most areas. Iran's rainfall anomaly in Phase 3 of Madden Julian Oscillation has shown, we have significantly limited rainfall and almost locally. In phase 3, there is no rainfall in Iran, and obviously, with the beginning of the positive phase of the Madden Julian Oscillation, which includes phases 3, 4, 5, and 6, the changes in precipitation in Iran are different from the negative phases 1, 2, 7, and 8, and their changes can be considered in relation to each other.Eliminating the boundary between spatial-temporal scales and a deeper understanding of distant planetary systems is always an important challenge that the scientific community is facing. Undoubtedly, progress in medium-range and seasonal weather forecasting and our understanding of large-scale weather patterns and the identification of specific causes of their occurrence rely on our deep understanding of the behavior of atmospheric-oceanic patterns and their relationship with each other. to be Based on the direction of this research, the investigation of precipitation anomalies associated with the eight phases of MJO during the period of December, January and February 1980 to 2020 showed that the precipitation behavior of each of these phases is different from each other and the position, intensity and extent under the influence These abnormalities are variable in each phase.

Clustering is an instrument that divides existing data into different groups. Generally, the number of clusters is determined based on the least changes within the group and the most changes outside the group. The study area is country of Iran. Coordinates of longitude, latitude, altitude, average temperature, relative humidity and total monthly rainfall of 420 synoptic stations from its establishment until 2018 have been used in this study. After reviewing, screening and repairing the data, only 375 stations remained to continue the research. Due to the length of the statistical period is an important factor influencing clustering, the stations are statistically divided into three periods: less than 5 years with 42 stations; 1-6 years with 33 stations and more than 10 years with 300 stations, were classified. Seven methods of hierarchical clustering (3 subsets), separation (2 subsets) and ward (2 subsets) have been used in this study. Cophenetic correlation coefficient, Silhouette width test are two indicators of clustering and selection. The coding was performed in R statistical software. Based on the Cophenetic and Silhouette coefficient indices, the best number and method of clustering for 1-5-year data are 4 clusters with the middle axis separation method, for the data of 6-10 years are 5 clusters with the mean-centered hierarchical method and for stations with a statistical period of more than 10 years are 4 clusters with the separation average axis method. The zoning of the clusters is plotted on the geographical map of Iran using ARCGIS software for all three categories. Keywords: Clustering, Geographical coordinates, Synoptic, Iran.Clustering is an instrument that divides existing data into different groups. Generally, the number of clusters is determined based on the least changes within the group and the most changes outside the group. The study area is country of Iran. Coordinates of longitude, latitude, altitude, average temperature, relative humidity and total monthly rainfall of 420 synoptic stations from its establishment until 2018 have been used in this study. After reviewing, screening and repairing the data, only 375 stations remained to continue the research. Due to the length of the statistical period is an important factor influencing clustering, the stations are statistically divided into three periods: less than 5 years with 42 stations; 1-6 years with 33 stations and more than 10 years with 300 stations, were classified. Seven methods of hierarchical clustering (3 subsets), separation (2 subsets) and ward (2 subsets) have been used in this study. Cophenetic correlation coefficient, Silhouette width test are two indicators of clustering and selection. The coding was performed in R statistical software. Based on the Cophenetic and Silhouette coefficient indices, the best number and method of clustering for 1-5-year data are 4 clusters with the middle axis separation method, for the data of 6-10 years are 5 clusters with the mean-centered hierarchical method and for stations with a statistical period of more than 10 years are 4 clusters with the separation average axis method. The zoning of the clusters is plotted on the geographical map of Iran using ARCGIS software for all three categories. Keywords: Clustering, Geographical coordinates, Synoptic, Iran.Clustering is an instrument that divides existing data into different groups. Generally, the number of clusters is determined based on the least changes within the group and the most changes outside the group. The study area is country of Iran. Coordinates of longitude, latitude, altitude, average temperature, relative humidity and total monthly rainfall of 420 synoptic stations from its establishment until 2018 have been used in this study. After reviewing, screening and repairing the data, only 375 stations remained to continue the research. Due to the length of the statistical period is an important factor influencing clustering, the stations are statistically divided into three periods: less than 5 years with 42 stations; 1-6 years with 33 stations and more than 10 years with 300 stations, were classified. Seven methods of hierarchical clustering (3 subsets), separation (2 subsets) and ward (2 subsets) have been used in this study. Cophenetic correlation coefficient, Silhouette width test are two indicators of clustering and selection. The coding was performed in R statistical software. Based on the Cophenetic and Silhouette coefficient indices, the best number and method of clustering for 1-5-year data are 4 clusters with the middle axis separation method, for the data of 6-10 years are 5 clusters with the mean-centered hierarchical method and for stations with a statistical period of more than 10 years are 4 clusters with the separation average axis method. The zoning of the clusters is plotted on the geographical map of Iran using ARCGIS software for all three categories. Keywords: Clustering, Geographical coordinates, Synoptic, Iran.

Rainfall time series analysis is important in that its variability is very high, and should be considered by water resources and agricultural management planners. In this study, the annual rainfall series analysis of three stations in Kerman, Sirjan and Rafsanjan as three major pistachio growing areas in Kerman province during the statistical period of 1986-2086 (35 years) has been investigated. First, the statistical parameters of precipitation data at three stations were examined, followed by the identification of precipitation behavior and norms. To evaluate the homogeneity of mean and variance, mean homogeneity test based on Standard normal homogeneity test and homogeneity of variance based on Van Neumann test were used. Investigation and detection of trends or no trends in the annual precipitation data of the studied stations using parametric methods (autocorrelation test, Pearson correlation coefficient and least squares error test) and non-parametric tests (Mann-Kendall statistical test, test The turning points and the correlation coefficient of Spearman and Kendall Tao) were investigated. The results showed that the precipitation data series in all three stations are heterogeneous in terms of mean and variance. Based on the parametric tests of Pearson correlation coefficient and the least squares error test in Rafsanjan station, there is a decreasing trend of precipitation in the study period. In Kerman and Sirjan stations, the slope of the precipitation line was 0.439 and 0.271, respectively, and the test of the least squares of error in these two stations showed that the slope of the line is not significant. Non-parametric tests of Kendall and turning point test also showed that there is no trend in the annual precipitation data of the studied stations and the null hypothesis test should be accepted.

The greatest environmental threat today is global warming and Climate Change. Climate Change deined as dramatic and long-term change in the distribution of Atmospheric patterns in the long-term periods. What distinguishes the current Climate Change is that humans have played an important role in the global warming process. The issue is that human activities increase the amount of greenhouse gases in the atmosphere. This will increase the amount of intense absorbed radiation, and the amount that is emitted again to the Earth causes the Earth to warm up. The weather model forecasts in the summary report showed that during the 21st century, global temperatures increased from 0/3 to 1/7 degrees Celsius to 2/6 to 4/8 degrees Celsius (4/7 to 8/6 degrees Fahrenheit), and its amount depends on the amount of greenhouse gases and the effects of Climate feedback. (NASA, 2017)The main effect of Climate Change is the increase of global average temperature. The average surface temperature could increase from (approximately 1/67 to 5/56 degrees Celsius) by the end of the 21st. This causes a variety of secondary effects, namely, changes in patterns of precipitation, rising sea levels, agriculture pattern changes, extreme increase of weather events, the expansion of the range of tropical diseases, and the opening of new marine trade routes. Potential effects include sea level rise from 110 to 770 mm between 1990 to 2100, agriculture consequences, possible slowing of the ocean heat circulation, reductions in the ozone layer, increases of intensity and frequency of extreme weather events, lowering of ocean pH, and the spread of tropical diseases such as malaria and dengue fever (IPPC,2013). The country Iran, which is a spatial political unit in Southwest Asia, is not excluded from the Climate Change impacts. Undoubtedly, Iran will face the consequences of the Global Climate Change today or in the very near future. Environmental impacts of Climate Change in Iran, considering that 90% of Iran's land are dry and semi-arid lands, leads to destruction of the agricultural industry. For this purpose, the following questions have been asked: Which indices of sustainable development in Iran are subjected to and how is the status of these indices validated by the country? What are the indicators of the effects of Climate Change on the sustainable development of the country and what is the status of these indicators in the provinces of the country?

Historically, Iran has always been exposed to the consequences of Climate Change, given its Geographical location. Iran's location in the Earth's desert belt and the availability of a quarter of its water resources (precipitation and surface water) have revealed the need to pay attention to the various dimensions of Climate Change in Iran. The purpose of this study is to zoning the country's regions based on Sustainable Development indicators, the effects of Climate Change, and to provide a model in different regions of the country. The present study is descriptive-analytical in terms of purpose, application and method. Data collection and documentary and survey information have been done with questionnaire tools. Data collection and documentary and survey information have been done with questionnaire tools. The statistical population of the research is purposeful and includes experts and specialists in the field of Climate Change working in meteorological, environmental and passive defense organizations. To ensure the validity of the questionnaire, the opinions of 3 professors of Climatology and Political Geography of Tehran University and Research Sciences were used and after making corrections and adjustments, 48 questions were compiled for the final questionnaire. To measure the reliability of the questionnaires, Cronbach's alpha test scored 0/8 out of 16 questionnaires as a pretest. The questionnaire was designed based on the indicators of the effects of climate change and after distribution, 60 questionnaires were collected. The data were then entered into the SPSS software and Friedman's comparative test was used to prioritize the Sustainable Development indicators of the effects of Climate Change.

Then, Friedman's comparison test in SPSS software prioritizes the effective parameters of Climate Change on Sustainable Development, which arerespectively, they are: 1- Vulnerability to livelihood, poverty and weak government 2- Sustainable Agriculture and combating desertification and drought. 3- Health, Public Health 4- Maintaining the balance of the natural ecosystem.5- Justice and social security and citizenship will have the greatest impact on the sustainabledevelopment of the country. Then, using GIS software, the country's zoning was drawn based on the effects of Climate Change in the country's regions.Country zoning based on poverty and social anomalies (misery index), Sustainable Agriculture (Strategic Products), drought and agricultural destruction, public health indicator, ecosystem balance, Social Justice (Distributive Justice)

The results of this study show that the provinces of Sistan and Baluchestan, Hormozgan, Qom and Alborz are in line with the highest percentage of weakness and fragility against the effects of Climate Change in the country, which will have the greatest challenge with drought and agricultural degradation These challenges will increase the process of importing agricultural products to Iran, and Iran will become an importer of these products, and on the other hand, will cause migration, unemployment, poverty and increase social anomalies in Kermanshah, Sistan and Baluchestan provinces. The government is also authorized to use educational services and the ability to reconsider using the revision and change in re-status using the revision in the library of Sistan and Baluchestan, Bushehr, South Khorasan, Alborz and Qom. To this end, the government can use a well-written plan to reduce the challenges posed by the effects of climate change in these areas. Finally, the need to implement effective methods such as watershed management and clean energy use to reduce greenhouse gas emissions to adapt to Climate Change impacts is emphasized.

One of major problems in large and industrial cities, such as Arak, Iran, is reduction of air quality. Variations in the Boundary Level Height (BLH) have a significant impact on air quality. The height and thickness of boundary layer changes at different hours of day and night and also on different days of year. Daily variations in temperature, humidity, wind, pollution and contamination, and turbulence and thickness of the boundary layer occur due to daily warming and nocturnal cooling of the Earth's surface, and a very stable layer with a temperature inversion always plays the role of the boundary layer cap. The present study aims at examining the variations in BLH different times of day over the months and seasons of the statistical term, and the effect of these changes on the reduction or increase of pollutants in Arak city.

Arak boundary layer daily and monthly data for the Different hours with a spatial resolution of 0.125degree arc from 1979 until the end of 2018, was collected from European Centre for Medium-Range Weather Forecasts. Variations in the Arak BLH over the statistical term were assessed and estimated using linear regression, non-linear regression, and Mann-Kendall statistical test. The air pollutant raw data of three air pollution stations across Arak city were obtained from General Directorate for Environmental Protection of the Markazi Province. After collecting the preliminary data, the air quality index (AQI) relation was calculated for five major air pollutants, namely suspended particulate matter, nitrogen dioxide, surface ozone, carbon monoxide, and sulfur dioxide, and then the responsible pollutant, i.e. the pollutant with the most AQI of other pollutants, was identified. To determine the influence of BLH variations on air quality, Pearson correlation test was administered between the BLH data and the five major air pollutant data.

The results showed that the BLH has an increasing trend in spring, summer and winter at all hours and it does not vary significantly in the autumn. Significant variations in the BLH of Arak city are different at night time and during daylight hours. In spring, on average the BLH increases by 9%, the highest increase being 18h. In this season, the BLH increases more at night than in daylight hours. In summer, on average the BLH increases by 6%, with the highest increase at 24h and the lowest increase at 12h. In this season, The BLH increases more at night than at daylight. The highest BLH occurs in winter, with an average increase of 34%, the highest being at 15h with an increase of 48%. Unlike both spring and summer, in winter the BLH increases more during the day than at night both. Overall, the BLH of the fourth decade increases by 9% in proportion to the average BLH of the whole yearly term, with the highest increase being 18% at 24h. To determine whether fossil fuels increase and land use and land cover changes of Arak city increase the BLH or whether global warming and macro-scale climate change increases the BLH of the cities including Arak, the BLH of four other points, besides those of Arak city center, were examined. The increase in Arak BLH is not much different from that of the rest, that is during the forty-year statistical term, the BLH increases to the same extent for both Arak city and its surrounding lands including the plain and agricultural areas, the mountainous areas, and the suburb. Therefore, it can be inferred that climate change and global warming have a greater impact on variations in the BLH.As stated in the data and methodology sections, the air quality index (AQI) relation was calculated for the five major air pollutants, namely particulate matter, nitrogen dioxide, surface ozone, carbon monoxide, and sulfur dioxide; The pollutants responsible for each days were determined for each station and particulate matter was found to be the pollutant responsible for most days at all stations in the Arak city.

The Arak BLH increases significantly in all seasons except autumn. This increase was not the same during daylight hours and months of the year, with the highest increase being related to winter, and the autumn of no significant change. The Arak BLH variations are in line with temperature changes in Arak city and it can be judged that the reason behind the Arak BLH increase is the increase in temperature of this city in the wake of global warming. The fossil fuelconsumption increase and land use change of Arak have a minor effect on the city BLH.The pollutant responsible in Arak city is particulate matter most of the days, originating in automotive fuel, industries in and around the city, and dustparticles. In the seasons when the dust masses do not enter the city, the BLH is negatively correlated with the suspended particles and on the days when the dust masses enter the city, the BLH relationship is positive and significant.The BLH increase in Arak city does not help much to reduce the concentration of pollutants in Arak city, as the BLH increase in some seasons is positively associated with ozone increase and particulate matter, and the mean of BLH is highly lower in winter, autumn, and other season’s nights so that it can naturally become polluted in a short time. As Arak is growing, it is increasingly being polluted and climate change is also occurring, and as urban adjustment and adaptation was repeatedly underlined in the Fifth Intergovernmental Panel on Climate Change. it is expected that the officials and policymakers develop and implement the urban development plans in accordance with natural and climatic features, and with adjustment and adaptation to climate change. The results showed that the correlation of the BLH with carbon monoxide and sulfur dioxide is negative, and with ozone and the suspended particles 2.5 and 10 microns is positive

The expansion of sustainable agriculture and the proper use of the country's water and soil resources require the selection of suitable plant species that are compatible with the climatic conditions of the country. Therefore, the optimal use of natural resources through the planting of plants compatible with the conditions of the region can guarantee human health, the sustainability and progress of sustainable agriculture, in fact, for the optimal use of land, the issues of water, soil and air cannot be analyzed separately For such researches, a framework such as zoning is proposed to plan, operate and properly use homogeneous pieces of land. Rosa damascene has a low water requirement and is highly adaptable to dry conditions. Considering Iran's climatic and geographical characteristics, the expansion of the cultivation of the Rosa damascene plant has gained double value.

Ardabil province in the north-west of Iran, with an area of 17,953 square kilometers, covers 1.09% of the total area of the country. Its geographical coordinates are between 37 degrees 25 minutes to 39 degrees 42 minutes north latitude and 47 degrees 3 minutes to 48 degrees 55 minutes east longitude. In the current research, using climatic criteria (Growth Degrree Days, annual precipitation, precipitation of the growing season, average temperature of the growing season, sunny hours of the growing season, and altitude) The growth of each plant starts from a certain thermal threshold, and the growth threshold of the rose is 2.5 degrees Celsius and the fuzzy TOPSIS method and the ANP network analysis process, the stations of Ardabil, Khalkhal, Parsabad and Meshginshahr have been selected for the cultivation Rosa damascene. The growth of each plant starts from a certain thermal threshold, and the growth threshold of the rose is 2.5 degrees Celsius. The steps of the TOPSIS technique include forming a decision matrix, forming a scale-free matrix, determining the Euclidean distance and choosing the best option, and the steps of the ANP method include creating a network structure between criteria, options and the goal, pairwise comparison of matrices and Formation of super matrix and selection It is the best option. In this model, each cluster and elements have a mutual relationship and the relationship of each criterion and clusters is checked with each other and they get a score between 1 and 9. The zoning and final drawing of the maps were done in the Surfer software environment and interpolation was done with the Radial Basis Functions method.

After weighting the effective parameters for the cultivation of Rosa damascene based on TOPSIS models and the ANP network analysis process and performing modeling and analysis of climate data, the final interpolation map for Rosa damascene cultivation was prepared based on the climatic potential and abilities. The results showed that in prioritizing the options for growing roses using the TOPSIS and ANP methods, the priority of the options is related to Meshgin Shahr, Khalkhal, Ardabil and Parsabad stations. In fact, the unrestricted lands include Meshgin Shahr station, which is the best place for cultivation due to its good climatic capabilities, and Ardabil and Khalkhal stations have low and medium restrictions for cultivation. The lands of this station have relatively weaker conditions than is the MeshginS hahr station , but good performance is expected from it. Parsabad station has very limited lands, which according to the assessment of climatic capability, this station lacks suitable potential for growing Rosa damascene.

In evaluating the ability of the habitat to plant a crop, not all criteria are equal. Some criteria play a key role. For this reason, factors are weighted in order to obtain a ranking of the value of decision criteria regarding suitable places for agriculture. The current research was conducted using TOPSIS and ANP methods and 6 effective atmospheric elements in the cultivation of Rosa damascene, standardization and formation of a ranking decision matrix based on these scores to determine and rank the Rosa damascene cultivation. These methods are more capable than other common methods. are doing the ranking more accurately. Finally, by interpolation (RBF) method, a map of the areas suitable for the cultivation of Rosa damascene was prepared. In the zoning map, there are very suitable areas for Mashgin Shahr station and unsuitable areas for cultivation at Parsabad station.

Today, air pollution due to continuous urbanization has become a global issue in both social and environmental fields, researches have been conducted in this field, Lim et al. in the capital region of Korea through regression modeling. The results indicate a relatively high concentration of NO2 in winter in the present and future forecasts, which is caused by the high use of fossil fuels in steam boilers and showed climate changes [1]. In 2021, Shams et al. evaluated the accuracy of multi-linear regression and multi-layer perceptron neural networks in predicting the concentration of NO2 in the air of metropolises. The results show that the multi-layer perceptron neural network had a more accurate prediction than the multi-linear regression [2].2- An overview of algorithms2-1- Crow's learning algorithmIn this algorithm, crows are trained based on two more optimal solutions which are parents. Another learning is the learning of each crow from its brothers and sisters, and the behavior of crows to hunt worms that are inside the tree trunk is used for modeling. In Crow's algorithm, parents X1, X2 reward their behaviors according to the following matrix. (1) F=2-2- adaptive neural fuzzy inference system ANFIS structure has a good capability in training, construction and classification. Its learning rule is based on the error backpropagation algorithm by minimizing the mean squared error between the network output and the real output. [3]. Figure 1- simple diagram of ANFIS [3] 2-3- Basal-radial neural networkRadial-based neural network is used for non-parametric estimation of multidimensional functions from a limited set of training information. In this network, the hidden layer plays an important role in converting non-linear patterns into linear separable patterns. which is in the form of relation (3):(3) "f" ("x")"=" ∑_"i=1" ^"p" ▒〖"w" _"i" "φ(" 〖"Xc" 〗_"i" "-x)"3- Steps of the proposed

All steps of the proposed method include pre-processing (cleaning, normalization and feature selection) and post-processing (proposed method).In this article, the data of Tehran meteorological station is used, which includes 1000 data samples with 23 features.Then, the fuzzy-neural adaptive inference system is used to predict the amount of nitrogen dioxide pollution. Crow learning algorithm is used to train this system. Figure 3- The structure of ANFIS neural-fuzzy inference systemTo select the parents based on the competence of the population members, the two crows that have the most competence are considered as parents.Figure 4- The learning phase and the new position of the crow after the learning phaseThen, in the evaluation stage, the objective function is called and the mean square error is calculated. Finally, the termination conditions of the iteration are checked based on the lower mean square error. 4- Simulation results 4-1- Prediction results with the proposed method The parameters of the population size of crows are 50 and the maximum number of repetitions is 500, the type of fuzzy inference system is Sogno type and Gaussian input membership functions are considered. Table 1- Types of errors in the proposed method in predicting NO2.Figure 5- Error histogram for training and testing data in the proposed method.Figure 6- Target outputs and outputs of the proposed method for training data.adial-basal neural network.Figure 10 - Target outputs and radial-basis neural network for training data.Figure 11 - Target outputs and radial-basis neural network for the entire test data.Figure 12 - Target outputs and base-radial neural network outputs for the whole data

This article is based on predicting the amount of nitrogen dioxide pollution using machine learning methods. According to tables (1) and (2), the fuzzy-adaptive neural inference system trained with the crow learning algorithm and the radial basis neural network performed the prediction with mean square error of 0.0081 and 0.0101, respectively. Therefore, the best performance belongs to the adaptive neuro-fuzzy inference system trained with the crow learning algorithm.

One of the important and fundamental needs in order to develop the capabilities and capabilities of tourism in a region is the suitable climate for tourism. The use of tourism capabilities and capacities requires the recognition and evaluation of comfort climate using accepted scientific methods to systematically determine the effect of climatic elements on the activities of tourists. Based on this necessity, in the current research, the spatial-temporal distribution of tourism climate conditions in Mazandaran province has been investigated using the Beach Climate Index in the ArcGIS software environment. In order to implement this index and achieve the goals of the research, the daily time series data of the meteorological stations of Mazandaran province during the years 1980 to 2018 were used. According to the needs of the model and method used, climate elements (on average) including relative humidity, wind speed, sunny hours, monthly precipitation, average daily and monthly temperature, cloudiness and number of foggy days have been used. After calculating the values of the CPI index in the J environment. Oh you. S, interpolation methods were used to prepare tourism activity zoning maps with the least amount of error in interpolation. The results of the coastal tourism climate index at the level of Mazandaran province on a monthly scale indicate that between the coastal and mountainous areas Different patterns prevail in terms of tourism comfort index at different times of the year. In autumn and spring, in the coastal parts of the province, especially the parts of the eastern coast, the quality of beach rest is in a better condition. Meanwhile, in the same period, cold stress prevails in the mountainous parts and reduces the quality of the climatic comfort index compared to the coastal areas. But in the summer months, due to the rule of heat stress in the coasts, the mountainous areas have a more suitable climatic comfort quality according to the BCI index.

Accurate spatial estimation of Precipitation and temperature is very important in hydrological models. Despite the development of automatic meteorological stations in recent years, obtaining reliable climate data in data-deficient areas is still a big challenge. Spatial estimation of climatic data is mainly done by geostatistical methods and satellite images. Nowadays, satellite products are widely accepted in the preparation of climatic maps. These products use satellite images to estimate temperature and rainfall data in points without data, and usually the provided data is accompanied by errors and needs to be recalibrated. It seems that the combination of covariates and satellite products can be effective in increasing the accuracy of climatic maps, especially in areas with complex topography such as Mazandaran province.

In this research, the accuracy of temperature and rainfall products of TRMM satellite was evaluated in Mazandaran province and the possibility of combining them with land features of latitude, longitude and elevation in the form of regression model was investigated. In this regard, the monthly rainfall data of 21 meteorological stations and 48 monthly and 4 annual images of TRMM products in 2014 and 2017 were used. The evaluation indicators are root mean square error (RMSE), mean bias error (MBE), mean absolute percent error (MAPE). Also, the annual temperature and rainfall maps of the province were drawn by satellite products and modified method.

The results showed that the TRMM products have a huge bias error, so that the amount of annual rainfall bias in some years reaches more than 180 mm per year. About the temperature products the underestimation error is more than 2 Celsius degrees. The correlation coefficients of land features and temperature and precipitation data in most of the months provided acceptable results and were significant in 95% confidence level. In general, the relationship between monthly temperature and, latitude and TRMM products was significantly positive in all the investigated months. in the case of altitude, the relationship was negative and strong. But the relationship between temperature and longitude was a little weaker than other covariates. Regarding the precipitation variable, satellite products have a positive and significant relationship in all the investigated months, and the altitude has a negative effect on precipitation data except in the spring months, but the latitude has a positive relationship in the cold months and in the warm months has almost a negative relationship, and no specific seasonal trend was found in the case of longitude. Also, the correlation coefficients of TRMM products with temperature and precipitation data was significant in 100 and 77% of the months, respectively. Investigating the possibility of combining the TRMM products with the latitude, longitude and altitude in a form of regression equation to estimate temperature and precipitation data showed that the hybrid method increased the accuracy of satellite productions, impressively and the error of rainfall and temperature products reduced by about 30% and 70%, respectively. But as expected, the spatial estimation error of precipitation data was higher than temperature in all investigated months. The annual rainfall maps of Mazandaran for the years 2014 and 2017 shows the higher accuracy of the hybrid methods compared to satellite products. So that it shows well the rainy area of the west coastline and also depicts the meridional and altitudinal gradients of precipitation in the Mazandaran province as well. Examining the annual isothermal maps showed that the drawn map with the correction method has a significant difference with the temperature product of the TRMM satellite and has well highlighted the ring of cold areas of Alborz Mountain range and the foothills of Damavand and Alam-Kouh peaks. Also, the modified map has correctly distinguished the temperate coasts of the southern Caspian Sea from the Middle Band and Upper Band regions. In addition, the higher temperature of eastern half of Mazandaran compared to the western half has shown well.

The results of the present research showed that the TRMM temperature and rainfall products alone do not have proper accuracy in the spatial estimation of climate data and have a large bias error, but their combination as a covariate, along with Longitude, latitude and altitude in a regression equation, improved the accuracy of temperature and rainfall maps, and can be used as a new post-processing method in modification of satellite products.

Temperature is one of the most important meteorological variables and any change in temperature variable causes changes in the occurrence of extreme phenomena such as drought, heavy rainfall, and storms that will cause irreparable damage in various social, economic, and agricultural sectors. Therefore, it is important to study the trend of these climatic variables in order to achieve methods for controlling and managing damages. Methods based on the mean or median of the data are generally used in studies related to trend investigation, since mean is a measure of central tendency, if studied alone may not provide information about trend variation in different parts of meteorological and hydrological data distribution, especially distribution tails. While extreme weather events often result from extreme values of climatic parameters. For this purpose, to study trend variation in the different data ranges, the quantile regression method was proposed, which has no limitations of previous parametric and nonparametric methods and has the ability to study trend variation and Show changes in different quantiles or different values of a climatic parameter. Therefore, the purpose of this study is to investigate the trend of temporal and spatial changes of minimum and maximum temperature on an annual scale using the quantile regression method in the geographical area of Iran.

The study area in the present study is the geographical area of Iran, which due to its location in the middle latitudes of 30 degrees, most of its area is covered by arid and semi-arid climates. In order to analyze a trend, maximum and minimum daily temperature data of 102 meteorological stations with a statistical period of 30 years (1988-2017) were obtained from the Meteorological Organization. After preparing the data, the annual time series was formed from the minimum and maximum temperature for this period of 30 years. Then the quantile regression method was used to analyze the trend variation in different quantiles of minimum and maximum temperature and the estimated slopes for the whole country were zoned using different interpolation methods in the GIS environment after that the Bayesian kriging interpolation method was selected for interpolation and the results were analyzed.

The results showed that the quantile regression method showed different trends for the minimum and maximum temperature variables in different quantiles and for different parts of Iran during the year. In general, both temperature variables had an increasing trend in all studied quantiles for all parts of Iran; Lower quantiles of the minimum temperature have an increasing trend in most parts of Iran and the most increasing trend slopes have been observed in the western half of the country, and about 63% of the area of Iran had a positive slope of 5-10%. While in the median quantile, the trend variation is more severe and all regions of Iran have a significant increasing trend that has been significant in most regions. in general, about 73% of the regions have a slope of 5-10%, which is visible in the western half, northeast, and southeastern parts and about 24% of the areas have a slope of 10-15% which is seen in eastern Iran. However, upper quantiles of minimum temperature that indicate high-temperature values also have a positive and significant trend in most parts of Iran, which in general 69% of the regions have a trend slope of 2-5%, which is located in the eastern half, north and south of the country, while 29% of Iran's area has a slope of 5-10%, which is mainly located in the western half and parts of the east and center of the country. However, in the study of the lower quantiles of the maximum temperature, the trend variation was more than the minimum temperature and there were significant increasing trends in most parts of Iran that 47% of the area had a slope of 2-5% which is located in the eastern half of Iran, and also 43% and 10% of the area of Iran had a slope of 5-10 and 10-15 %, respectively, which were observed in the western half of the country, but the number of increasing slopes was higher in the west. The median quantiles of the maximum temperature have a slope of 5-10% in 73% of the area, and 24% of the areas have a slope of 10-15%, which was significant in all cases. However, for the upper quantiles of the maximum temperature, trend variation was not significant, so that 64% of the area had a slope of 2-5% in the southern half and 36% of the areas had a slope of 5-10% in the northern half of Iran.

The most increasing trends for low values of minimum temperature were in the western half, median values in the eastern half, and high values in the western half, east and central part of Iran. In contrast, the highest upward trends for low values of maximum temperature are obtained in the northwest, median values in the eastern, western, and central half, and high values in the northern half of Iran. trend slopes for both minimum and maximum temperature have been higher in the median quantile and in general, it can be inferred that the temperature in Iran has increased due to climate change and the quantile regression method is useful to study and control very high and very low temperatures that are more important than the average temperature in climate risk studies.

Meta-heuristic algorithms are problem solving methods that are modeled on the events in nature or the behavior of living beings so that optimal solutions can be extracted. Collective intelligence algorithms [1] are a kind of meta-heuristic algorithms that are modeled on the behavior of living beings that live in a group and social life, such as hunting behavior, hyena optimization algorithm, whale optimization algorithm, etc. Is. Meta-heuristic algorithms can be divided into different categories based on the method of problem solving, one of which is shown in the research [2] in 2020 according to the diagram in Figure (1) and can be seen. Meta-heuristic algorithms are divided into 4 different groups and categories based on their performance:Figure 1: Classification of meta-heuristic algorithms into different categories [2]Meta-heuristic algorithms are used in various fields, one of which is the optimization of machine learning and deep learning parameters. One of the applications of machine learning and deep learning is in weather forecasting. In this article, to improve the accuracy of the LSTM network, the optimization of important features using the learning method incrows has been used.2. LSTM learning network In the short term, long memory neural networks are actually a type of recurrent neural networks [3].In the LSTM network, with the help of the sigmoid function that is applied element by element, the input, forgetting and output gate layers produce vectors whose all dimensions are between zero and one or close to both. The general structure of LSTM deep learning neural network is as shown in Figure (2):Figure 2. The structure of long memory networks, in the short term 3. proposed model Figure (3). the framework of the proposed method is shown. The evaluation or minimization function of the following two factors shows how well a feature vector has competence:• Average prediction error with neural networ• Number of features selected The calculation of error index E is as follows:the population of crows is stored in a matrix:Each crow needs to remember the most optimal position:In the crow learning algorithm, there are two phases of horizontal and vertical learning. Vertical learning from parents is horizontal learning from brothers and sisters. It is used to select a sister or vector randomly. 6 k= 3+[rand×(i-3)] & i≥3 In the crow's learning algorithm, the probability of receiving a reward for crows is equal to Rpprob. lf is the value of the learning factor in crows. Amount of reward for crows: Reinforcement of learning for parents is used in the Crow algorithm as follows. It is used to search for food with the stealing mechanism as follows: 4-Implementation and analysis 4-1-Implementation parameters Table (1): Implementation parameters of the proposed method Figure 4: LSTM implementation parameters in the proposed method 4-2-Evaluation indices One of the important indicators for predicting weather conditions is the mean squared error MSE index, and to evaluate the proposed method, you can use the classification and prediction indicators of accuracy, recall and accuracy: 4-3- Analysis of the proposed

In the diagram of figure (5), the prediction error in the feature selection phase in combination with the neural network is shown, and in figure (6), the output of LSTM deep learning in weather forecasting is depicted. Figure 5: Reduction of prediction error in feature selection phase with 10 iterations Figure 6: Reducing the prediction error in the classification phase with LSTM Table 2: Average prediction indices of the proposed method Figure 7: Comparison of the MSE error of the proposed method with predictionmethods Figure 8: Comparison of the accuracy of the proposed method with prediction methods Figure 9: Comparison of recall of the proposed method with prediction methods Figure 10: Comparing the precision of the proposed method with prediction methods Figure 11: Comparison of the accuracy index of the proposed method in weather forecasting 5.

LSTM network is a deep learning method that can be used to predict weather conditions. In the proposed method to increase the prediction accuracy of LSTM neural network, intelligent feature selection is used using a combination of crow learning algorithm and crow search. Experiments showed that the proposed method has an accuracy of 96.92%, a sensitivity of 95.82%, and an accuracy of 96.34%, and it is more accurate for predicting weather conditions than multilayer neural network, recurrent neural network, and LSTM method.

Undoubtedly, one of the areas in which drought has a great impact is the water resources of each country, and the areas that have the capability of drought are more limited and sensitive to water resources and require measures before, during and after the drought(Lain and Adamowisky,2012). While the dependence rate of agricultural lands equipped for irrigation on underground water sources is 37.8% on average in the world, the said coefficient is 46.2% in the Middle East region and especially in Iran it is equal to 62.1%. This is a confirmation of the high share of the agricultural sector of groundwater consumption in Iran compared to other countries and even the Middle East region (FAO, 2009). About 85% of the surface of Iran has a dry desert, semi-arid and ultra-arid climate (Ghafouri, 2003). Also, the annual rainfall of Iran is 240-260 mm and less than one third of the average annual rainfall of the world (870 mm). The phenomenon of drought during its occurrence period affects the underground water resources, which unfortunately has been less noticed. One of the important effects of drought is related to the drop of underground water, which due to the lack of rain and snow, there is a sharp decrease in the nutrition of the aquifers, and with the excessive exploitation of these aquifers, the condition of severe drop. Water resources are provided at the water level.

The research area is located in the north of Lake Urmia and the watersheds overlooking the city of Tusuj in the coordinates of 45° 18’ to 45° 33’ east longitude, 38° 20’ to 38° 24’ north latitude. According to different classification methods, the climate of the region is cold, semi-arid, and it is considered a Mediterranean rainfall regime.In this study, one of the important drought indices that is widely used in drought monitoring is the standardized precipitation index (SPI), which was presented by McKay et al.(1993) to determine the probability of drought occurrence. To model the monthly rainfall data, one of the suitable statistical distributions for this task is to use the gamma distribution. The reason for using this distribution is the nature of the statistical distribution itself, in which values close to zero have a frequency with greater probability, and the same nature is also valid for monthly rainfall data (the existence of months without rainfall). In this study, the correlation between the SPI index and the GRI standard groundwater index is investigated and the effect of drought on the GRI index is evaluated with a regression model.

According to the results of the correlation coefficient table, the GRI index has a significant positive correlation with the SPI drought index in the time scale of 48 months for Tasuj station. For Sharafkhane station, the SPI index has a significant negative correlation at a significance level of 0.05 in the time scale of 12 and 24 months. For Khoi station, in all three time scales of 12, 24 and 48 months, there is a significant negative correlation at the significance level of 0.05 and even 0.01. The 48-month drought index of Tasouj, 12-month Sharafkhane and 24-month Khoi, which have the most significant correlation with the GRI index, were entered into the regression model as factors influencing drought to determine the total changes in GRI that were affected by the drought conditions of three stations, to be determined. Therefore, three drought indexes as three independent variables and GRI index as a dependent variable form a linear multivariate regression model.According to the results, the correlation coefficient (R) and the coefficient of determination of the model (R Square) are 0.706 and 0.499, respectively, and this indicates that 49.9 percent of the changes in the model are determined or covered by independent variables. A value of 0.000 for Sig. In the analysis of variance (ANOVA) table, it shows the significance and validity of the model. By having regression coefficients, it is possible to reach the relationship between the GRI index and the drought indices of the stations. which is mentioned in relation 1. All coefficients of the model are significant (Sig.=0.000) and indicate the validity of these coefficients. Therefore, the regression relationship of the model can be shown as follows. GRI=0.240+0.923×SPI48(tasouj)-0.235×SPI12(sharaf)-0.559×SPI24(khoy) (1)

The results of the study of the effect of drought on the underground waters of the Tusuj aquifer area indicate a significant correlation between the 24-month drought profile of the Tusuj station, 12-month Sharafkhane and 24-month Khui stations with the GRI standard groundwater profile of the Tusuj aquifer area. The downward trend of the GRI groundwater standard index is consistent with the downward trend of the drought of the Tasuj rain gauge station in three time scales of 12, 24 and 48 months. Of course, despite the fluctuations of the SPI drought index, especially the short-term increase and the occurrence of insignificant drought in some years in Sharafkhaneh and Khoi stations, but due to the accumulation of drought and the dominance of drought years over drought years on the long-term climate memory The region has been affected and has caused a decrease in groundwater and a downward trend in the GRI standard index.For a more accurate statistical analysis, a suitable regression model was determined between the GRI index as a dependent variable and three drought index variables of 48 months of Tasouj, 12 months of Sharafkhane and 24 months of Khoi as dependent variables. which is given in relation (1) as the result of the regression equation. By using this relationship, it is possible to calculate the values of the corresponding GRI standard index by having the SPI drought index values.

The position of Iran between extratropical and subtropical latitudes, the location of a major part of Iran on the desert belt of the world from North Africa to Central Asia, and the role of various geographical phenomena in Iran have caused all kinds of atmospheric hazards in this region. To have an accident (Alijani, 2018). Such risks are inherent in Iran's climate and have occurred regularly since the distant past, as the natural environment and human activities in different regions of Iran have adapted to these phenomena. As the behavior of these phenomena has been repeated somewhat regularly and the climatic changes and short-term atmospheric processes have been less. These phenomena include droughts and droughts, heavy rains and floods, floods and dust storms, frosts, heat and cold waves (Omidvar-Kamal 2013). But in recent decades, with the increase in the temperature of the earth after the industrial revolution (1850), and with the intensification and rapid trend of temperature in the recent decades from 1970 onwards, the atmospheric hazards in the world, including Iran, have become abnormal. Such heavy and short-term rains cause a lot of damage related to agriculture, severe soil erosion, and destruction of transportation infrastructure and flooding of cities and villages. In recent years, in connection with global warming and changes in the behavior and anomaly of rainfall in the world, the amount of rainfall has decreased in this region, but the rainfall has mainly changed its behavior to heavy and short-term rainfall. Therefore, knowing the patterns that lead to the occurrence of these precipitations will be useful in the first place for predicting and managing precipitations. The aim of this research is to identify and analyze the atmospheric circulation patterns leading to the occurrence of heavy rain and short-term flooding based on the data of the stability rain gauge in Khorasan.

in this study, the daily rainfall data and the rain logger of the stability station were used. Among the stations in the region, those that have a proper distribution in terms of spatial distribution and also have long-term statistics (1993-2017) were selected. The synoptic stations were chosen to cover all the regions of the province with different topography and climate diversity. In order to determine heavy rains, the 95th percentile index of daily rains in each station was used. In the following, days with heavy rainfall and more than 95th percentile were extracted in each of the stations using these indicators. The spatial distribution of these rainfalls in the whole province was determined by using the number of these rainfalls in the entire region. , heavy rain. In connection with the purpose of this study, determining the atmospheric patterns of heavy rainfall in Khorasan, after determining the days with heavy rainfall in the previous stage, the atmospheric patterns of rainfall were determined and synoptically analyzed. For this purpose, reanalyzed daily grid data with a resolution of 2.5 degrees were extracted from the National Center for Environmental Prediction and Atmospheric Research (NCEP/NCAR) for the above days. The selected window for receiving network data in this study is 15 to 70 degrees north and 15 to 80 degrees east, so that the patterns affecting Khorasan precipitation in this range can be identified.

On February 17, 2017, in the geopotential height map of the middle surface of the atmosphere, a trough is drawn over the entire western half of Iran and the western coast of the Caspian Sea, and the center of this trough is a closed cell with a height of 5280 geopotential meters over the northeast of the Caspian Sea and Kazakhstan. This deep trough extends to Saudi Arabia and passes through the center of Saudi Arabia at a height of 5750 meters and shows the penetration of cold air in the upper atmosphere to the warm southern sea of Iran. At the same time, in the east of the Mediterranean Sea, there is a Rex-type blocking. These atmospheric conditions caused persistence and slow movement of the circulation system in the synoptic scale in Southwest Asia. At the same time, there is a low pressure center with a pressure of 1010 HP in front of the curved ship in the east of Iran. The east of this trough extends from the southwest and the western half along the southwest to the northeast direction of Iran, which caused instability in the northeast of Iran. On April 1, 2016, the arrangement of westerly winds in the entire Eurasia area shows a large anomaly in the westerly wind waves, naturally in late winter and early spring, due to the reduction of temperature and pressure differences in the northern hemisphere, westerly winds move meridian. And the number of long waves also increases hemispherical. In this map, there are two meridional patterns of westerly winds in the north and south, the first pattern in the north with a deep and curved channel with a west-east direction and closed with two equal heights, cut off low respectively over Western Russia and Kazakhstan respectively with There are 5280 and 5440 meters, That on the surface of the earth in these two areas, two low pressures with a pressure of 1003 and 1011 HP, respectively, indicate a strong rotation and instability in these areas.

Heavy rains and floods are one of the most important weather hazards that cause great damage to nature and humans every year. This type of precipitation, which is an inherent feature of arid and semi-arid climates, has increased significantly in recent years due to global warming and the increase in climate extremes.

Temperature changes are an important issue in the present age and it is a new topic for climatologists. The purpose of this research is to monitoring the changes in the biomass of trees during the growth periods of pole, beam, vigorous and old by using temperature characteristics. In this research, 307 parcels were studied. In this research, 30 sample plots were implemented in Parcel 307 using regular random method. Then, using a drone, the temperature of the centers of the samples was measured. In this research, using the mode of the tree mass was measured during the growing period of the trees, the trees, the trees, the stout, and the old trees. In these models, temperature was considered as the main variable and biomass was considered as the dependent variable. The preliminary results of this research showed that F. orientalis trees have a correlation coefficient of 0.96, 0.97, 0.96, and 0.94. The obtained results showed that Q. Castanifolia trees have correlation coefficients of 0.79, 0.81, 0.96 and 0.64. The results of A. glutinosha trees showed that the correlation coefficient of T.subcordata trees was 0.75, 0.99, 0.99 and 0.41. The results obtained from this show that deciduous trees have a correlation coefficient of 0.82, 0.19, 0.19 and 0.19. The results of this research showed that the use of the temperature characteristics of the sample centers has high accuracy in determining the biomass of trees in various vegetative stages.