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Today, energy consumption plays a decisive role in the qualitative and quantitative development of human life. One of the energy sources is in line with the development and economic prosperity as well as acquisition of climate comfort in natural gas. This source of energy supply, especially in cold areas of the country as well as to provide thermal comfort is required to proper management. Good management of this fossil energy sources is through awareness and forecasts of demand rate of that path. For this reason, demand for natural gas in Zanjan city was studied and modeled as one of the cold cities in Iran. Two groups of data (weather elements and natural gas consumption) for a period of 9 years (2013-2021) on a daily scale have been used for performing this study. CurveExpert software and regression method (regression) were used in order to modeling the demand for natural gas in the city. Based on the most graceful pattern, temperature element was selected as the only independent variable in selective pattern. Polynomial regression with a correlation coefficient of 0.94 (coefficient of determination 89.03 percent) was selected to the final model. The percentage increase in natural gas consumption per one degree decrease in temperature, from 22 to 16 degrees, the highest percentage increase and from zero to -5 degrees, the lowest percentage increase per one degree decrease in temperature. The turning point and the beginning of problems related to the lack of natural gas in Zanjan city was determined to be below minus 7 degrees.

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All the water vapor of atmosphere is contained in a column of the atmosphere that is capable of precipitation and it is from the ground to the final of water vapor called perceptible water. This element influenced by topography and height. The purpose of this study is survey about impact of local and spatial factors on distribution of perceptible water maximums in Iran.For this reason, pressure data, especially moisture, orbital and meridional components extracted from NCEP/NCAR and analysis. Correlation and regression methods were used in this study. In order to better survey about perceptible water gradient changes and gradient changes of maximum of perceptible water has been calculated. Results showed that among the spatial factors, height has greatest impact on the spatial distribution of the maximum of perceptible water. Unlike many scientists who believe that by increasing the latitude perceptible water reduced, this rule is less In Iran atmosphere. However, most of the gradient changes of perceptible water occurred in some parts of the Zagros highlands, West and South West. The results of cycle analysis showed that the maximums of perceptible water in Iran have short term cycles between 2 to 4 years.

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Trees can record long-term effects of climate variables. Using dendroclimatology knowledge, we can reconstruct such variables especially for areas which have short-term climatic data. For this purpose, we reconstructed the temperature degree of the warm months (May-September) through annual rings width of Quercus persica and regression analysis of data obtained from stations on Dena region. With this goal in mind, three growth heights were selected in Dena Forests and 52 growth samples from 26 bases were extracted and their growth rings width were measured with LINTAB5 with an accuracy of 0.01 mm. After cross dating stage, to eliminate non-climate effects, May to September temperature average and tree rings time series were standardized. The Residual Chronology (RES) calculated by ARSTAN was calibrated with temperature degree of the period 1882-2011 and its positive and significant correlation with the width of growth rings was confirmed. Based on the relations between the calculated chronology and joint statistical temperature degree data, the reconstruction of temperature degree of the warm seasons for over a century was performed and it was found that in the last three decades, region's average temperature of May to September had an increase in comparison to the average of the previous century.  

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  Climate control centers in each area are diverse and understanding how they relate to the atmospheric components of the Earth's surface contribute to prediction of climate fluctuations.   In this study, by using Pearson's correlation and multivariate regression in a thirty-year period (1961-2010), the relationship between widespread rainfall anomalies in entire of Iran west with temperature and pressure of atmospheric centers in East and West of Mediterranean Sea in 5 atmospheric levels (SLP, 850, 500 and 300 Hpa) were analyzed and modeled. Based on the results, the correlation of atmospheric control centers in the East and West Mediterranean Sea with anomalies of rainfall in West of Iran is inverse and meaningful in 95% level. In this study, statistical indicators such as temperature differences and standardized  pressure between West and East Mediterranean sea were identified as the most important indicators in relation to changes of rainfall in the study area.   Based on the designed indicators, whenever indicators DT and DH is positive, this means higher temperature and higher atmospheric standardized pressure in the Western parts of Mediterranean sea in compare with its East and therefore the wet spells (Monthly) occur in the study area, and If the above mentioned index is negative, means that the occurrence of drought in West Iran. As for the indicators introduced for lower levels of the atmosphere, especially in the case of temperature, meaningful strong and direct correlation is seen with rainfall abnormalities in entire West of Iran. Modeling provided some indicator for Mediterranean region using multivariate regression that they showed a relatively strong correlation in this regard of the selected components that include the pressure difference in sea level, the temperature difference in 925 and 850 hPa level in the West (Compared to its East) Mediterranean sea. Also check the regression model using real data confirm the accuracy of the relative performance of the model.  

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According to urban environment complexity and dynamism and need to targeted land use change, incorporation GIS and PSS in the form of Spatial Planning Support Systems is inevitable. The aim of this study is to develop a spatial planning support system for urban land uses change (ULCMS), such that planners can enter expert knowledge in the form of desired criteria and weights and see their influence in results. The developed system including modules for land suitability evaluation, calculation of the area of required land and land use change. Access models, neighborhood models and Multi Criteria Decision Making methods, fuzzy operators, linear regression, maximum potential and hierarchical optimization models is used in planning and implementation these modules. System practical test performed for measuring residential, commercial, industrial, agriculture and service land use changes for the year 1390 and 1395 in Shiraz city. The result shows that ULCMS help users in better understanding, showing complexity of land use system and development and improvement land management strategies for the creation of better balance between urban expansion and environmental conservation.

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Thunderstorms are major climatic events due to the significant effects and catastrophic consequences on humans and the natural environment. The researches have shown that the elevation and latitude factors are two variables that can affect the occurrence of this phenomenon. Therefore, the main aim of this study is to investigate the spatial analysis of the effects of lightning and its effects on the components such as elevation and geographic extent in Iran. Apart from this fact, firstly, the monthly data of thunderstorms occurrence in 118 synoptic stations of Iran, from 1991 to 2010 on a basis from the country's meteorological organization were obtained and GIS software was produced by the annual and seasonal maps of Iran. Then, for the spatial analysis of this climatic phenomenon, the method of landing statistics of the Kriging (Universal) method was to examine its seasonal and annual status. In order to better understand the effect of Thunder hurricanes from altitude and latitude using Curve Expert software, seasonal and annual charts, along with the correlation of each production, were analyzed. The results show that the highest annual thunderstorms occur in the northwest of Iran, and the least amount is consistent with the central and eastern parts of the country. In addition, according to seasonal analysis, although the station has the highest rate at 800 to 1,300 meters, the maximum occurrence of this phenomenon varies from 0 to 2200 meters in different seasons of the stations. The overall result shows that the factor of height is slightly correlated with the occurrence of the Thunder storm phenomenon and the highest correlation is due to the latitude factor.

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Estimating and predicting precipitation and achieving its runoff play an important role to correct management and exploitation of basins, management of dams and reservoirs, minimizing the flood damages and droughts, and water resource management, so they are considered by hydrologists. The appropriate performance of intelligent models leads researchers to use them for predicting hydrological phenomena more and more. Therefore, in this study, the Gene Expression Programming (GEP) and Support Vector Regression (SVR) models were used to model monthly precipitation of Nahavand City. In this study, precipitation, temperature, and relative humidity data were used in a 32-year period (from 1983 to 2014). The results showed that the same and good performance of both models (R²= 0.92), but according to different evaluation criteria, GEP model showed a little better performance (RMSE= 0.0478 and 0.0486), while the running GEP model is so easier than the SVM model. Totally, it can be said that GEP model had been suitable for modeling monthly precipitation of Varayeneh station in Nahavand City. Finally, the monthly precipitation was predicted the GEP which showed a decrease in precipitation in compared with previous months.
 

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owledge of spatial rainfall behavior in environmental, land planning is effective. These changes in the later place in the form of time later and in the climate of the area. The Target of this study was to reveal the presence or absence of precipitation trend in the ratio of the height of local precipitation behavior and identify province mazandarn. Therefore, the purpose of the rainfall data station 32 (Meteorological Agency and Department of energy), the statistical period 1988-2010. To get the regression analysis of precipitation process was used to identify the local behavior of precipitation, the method of spatial statistics were used. The results obtained from the behavior of precipitation, the existence of the process within the scope of the study and the emphasis is most consistent with the Be modified regression model at adjustment indicate. According to the regional behavior of precipitation, using local spatial statistics, spatial Moran well hot spots check this behavior. The results showed that precipitation in the province of Mazandaran has the pattern of clusters with high value. According to the local hot spots and methods Moran, West Coast up to a height of 700 m has positive z score and clusters with high value, 99% confidence level. This range includes 15% of the total of the province. The range of the Southern Highlands as well as the negative z score and clusters with low value with a confidence level shows 99%. This range is also about 20 per cent of the province's total. About 65 percent of the total area of the province as well as the lack of a significant trend show.

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The purpose of this study was to estimate the amount of sediment of Vanai River in Borujerd. In this research, the characteristics of the sub-basins of this river have been extracted first. These specifications include the physical characteristics of the sub-basins, including the area, the environment and length of the waterways, and the characteristics of the river flow, and its sediment content. In the following, multivariate linear regression, multilevel prefabricated neural network (MLP) and radial function-based neural network (RBF) models are used to model sediment estimation. After estimating the model, the mean square error index (RMSE) was used to compare the models and select the best model. Evidence has shown that initially the MLP's neural network model had the best estimate with the lowest error rate (90.44) and then the RBF model (151.44) among the three models. The linear regression model has the highest error rate because only linear relationships between variables are considered.

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Subsidence is an environmental phenomenon caused by the gradual subsidence or sudden subsidence of the earthchr('39')s surface. The phenomenon of subsidence in residential, industrial and agricultural areas can cause catastrophic damage. In most parts of Iran, there is a high correlation between land subsidence and the decrease of groundwater level and consequently the density of soil layers. In this study, using two time series of radar images with artificial apertures from Sentinel sensors belonging to 2014 and 2019, the amount of subsidence in Damaneh plain (Frieden city) was calculated. Wells were studied in the period 2014 to 2019, the results of the study of the correlation between land subsidence with changes in groundwater level at the level of 95% was significant. In the continuation of the research, using the logistic regression model, the subsidence trend in the study area was predicted and a subsidence probability map was prepared and created as a dependent variable for the logistic regression model. The independent variables used included altitude, slope, slope direction, geology, distance from the road, distance from the river, land use, distance from the village, groundwater level, piezometric wells. The output of the model is subsidence risk zoning map which was created in five classes. The accuracy and validation of the logistic regression model was evaluated using the system performance characteristic curve and the accuracy (0.89) was obtained. The good accuracy of the logistic regression model in producing the probability map Subsidence is in the study area. In the output of the model, it was found that the area of ​​1980 hectares, equivalent to 7.9%, has a very severe subsidence that has put the situation in a dangerous situation and the need for control and management to reduce this destructive effect.

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This research study aims to investigate the effect of climatic variables, specifically precipitation, temperature, and humidity, on changes in vegetation indices of orange orchards in Hassan Abad, Darab County, using satellite data. Consequently, observational data, including orange tree phenology data and meteorological data from the agricultural weather station, were collected over a period of more than 10 years (2006 to 2016). MODIS images from 2006 to 2016 were referenced based on territorial data and 1:25000 maps from the Iran National Cartographic Center. These images were used to calculate remote sensing vegetation indices, namely the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). The results demonstrated that the variables of maximum humidity, minimum temperature, and precipitation have a significant positive effect on the NDVI variable. Additionally, the variables of maximum temperature and minimum humidity have a significant negative effect on both the NDVI and EVI. To determine the significance of each independent variable in predicting the dependent variables, the artificial neural network method was employed. The findings showed that the climatic elements of precipitation, minimum temperature, maximum temperature, minimum humidity, and maximum humidity had the greatest effect on EVI, with values of 0.39, 0.3, 0.13, 0.1, and 0.06 respectively. Moreover, the effect of these variables on the NDVI index is equal to their coefficients, which are 0.2, 0.28, 0.22, 0.11, and 0.17 respectively. Finally, the ARMAX regression method was used to improve the explanatory power of the model. The results indicated that this method enhanced the explanatory power of the model and reduced the forecasting error.