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 series

The 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