Air temperature is one of the most frequently used parameters in the assessment of climate change at global and regional scale. So researchers have tried to modeling and predicting it with different models. This study also aims to model and predict the country's monthly minimum and maximum temperature. Investigates of temporal temperature changes is done by Sen’s estimator and Pettit method and predicting made by Holt-Winters model. The results indicated that the minimum temperature during 1961 to 2010 increased by 2.9º C . This rate is more in stations located in the warm and dry regions (3.1°C) than any other stations (1.8°C). While the maximum temperature gradient changes are lower and is about 2.1°C. The results also confirmed the performance of Holt-Winter's forecasting model. Beside a few exceptions, the minimum and maximum temperature will be increased until 2020. The highest increase of temperature will occur in Khoy, so that the minimum and maximum temperature will be increased about 0.6°C and 0.28°C, respectively.

In this study, the interaction between atmosphere and earth surface and its effect on the simulation of Sistan wind structure in the East of Iranian plateau is investigated. For this purpose, four experiments have been carried out with RegCM4, with horizontal resolution of 20 km. In non-topography experiments, the model was implemented in three different conditions. The results indicated that the Sistan wind is a multi-scale climate phenomenon which will be affected by topography both in wind intensity and in wind direction in the lower troposphere. In the synoptic scale, the pressure gradient which dominates between Pakistan heat low and the Turkmenistan anticyclone (Caspian Sea high pressure) can create a large scale background northerly flow in the lower troposphere which will be passing through the whole area in the east of Iranian Plateau. Furthermore, in meso to regional scales, the topography of the area would be responsible for creation and maintenance of a Low Level Jet (LLJ) through a mechanical and thermal forcing. the mechanical forcing of mountains are responsible for appearance of two LLJ cores across eastern borders of Iran which their preference locations would be around Atishan Desert in the north and upstream of Hamon Lake in the south. As a general result, by eliminating the topography in all non-topography experiments, the LLJ core will disappear on upstream of Hamon Lake as the most important mechanical forcing of the mountains. However, eliminating only the southern Khorasan Mountains will accelerate the north LLJ core in the Atishan Desert, while the LLJ core on the upstream of Hamon Lake will disappear over the Iran Borders at the same time. To evaluate the influence of thermal forcing of the mountains on Sistan wind structure, the total heating, as a residual term of thermodynamic equation, is calculated. The results indicate that mountains have a significant role to building a local low level circulation in the east of Iranian Plateau.

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.
 

Human thermal comfort and discomfort of many experimental and theoretical indices are calculated using the input data the indicator of climatic elements are such as wind speed, temperature, humidity, solar radiation, etc. The daily data of temperature، wind speed، relative humidity، and cloudiness between the years 1382-1392 were used. In the First step، Tmrt parameter was calculated in the Ray Man software environment. Then UTCI and PMV index values were calculated using Bioklima software. The results showed that the most severe cold temperature stress on PMV index is in the winter and late autumn and UTCI index in January and February are the coldest stress. The power of neural networks, prediction of future performance network (generalized orientation) it simply is not possible and the new model presented in this paper have been restricted Boltzmann machine-based neural networks or neural networks is used deep belief. Using this structure, metrics Mean Squared Error (MSE) and mean absolute percentage error (MAPE) benchmark ate for seven indexes derived from data gathered by three factors related to the occurrence of weather conditions and other indicators of thermal comfort of human the system was evaluated. Assessment by dividing the data into training and testing parts, and the ratios have been of two-thirds, fifty percent and one-third And two benchmark MSE and MAPE were calculated. The proposed system performance in forecasting the human thermal comfort is desirable.

Solid waste is an integral part of human life, generation of various quantities and qualities of which is one of the greatest environmental problems of the current era. Application of modern tools and technology to find the right place for recycling, where the volume of recyclable materials can be reduced, seems vital for reusing the recoverable materials contained in normal waste and their recycling and reusing in the cycle of industry. The study aimed to find a location for recycling and its converting industries in Kelardasht, Iran. This was a descriptive and analytical research with a survey approach. At first, experts of the field were recognized and a scientific panel was formed to identify the relevant criteria through Delphi method. In this respect, five criteria of the distance from residential and commercial areas, the distance from urban streets, the distance from the river, the distance from the hospital and education centers, and the distance from hotels, banks, and offices were selected. After that, the permitted distances for these criteria were determined by Delphi technique, followed by the application of the centroid method to determine the exact distances. Moreover, the analytical hierarchy process (AHP) and geographic information system (GIS) were exploited to determine the and use weights in raster maps and generation of the zoning map. Furthermore, the best locations for recycling were selected in Kelardasht using field survey method and based on the zoning map. According to the results, four sites were selected for recycling of recoverable solid waste in this region

Greenhouse emissions from agricultural as a consequence of human activities are causing climate change. Small changes in agricultural practices have a large potential for reducing greenhouse gas emissions. Intention of Agriculture Experts in order to mitigation climate change Can play an important role in finding appropriate solutions for each region in climate change mitigation and deliver them to their farmers. However Intentions to take mitigation actions are, most likely, influenced by a range of factors. The purpose of this study was to investigate of factors influencing on Agriculture Experts in order to mitigation greenhouse gases. This research in terms of purpose is an applied research and in terms of methodology is survey research. The statistical population of this research consisted of 520 Agriculture Experts in Khuzestan Province. A sample of 320 persons was selected through Random Stratified Sampling method. The survey instrument was a questionnaire which its validity confirmed by experts. The reliability of the questionnaire using Cronbach's alpha coefficient (α= 0/67-0/87) has been approved. Our analysis reveal, direct experience were predictors of risk perception, self-efficacy and environmental attitude. The changes in self-efficacy and environmental attitude were also strong predictors of changes in intention.  The variables of self-efficacy and environmental attitude could predict 61 percent of variations in willingness to mitigating climate change. Therefore experience indirectly affected on the mitigation intention. Lastly we provide recommendations on how these findings can be used to climate change mitigation.