Spatiotemporal Analysis of Long-Term Rainfall in Semi-Arid Area Using Artificial Intelligence Models (Case Study: Ilam Province, Iran)

Abstract

Ilam province is located in the southwest of Iran, and the primary motivation for this research is to study different dimensions of rainfall fluctuations in the Ilam province. This study is of great importance for the management of the environment in terms of the application of rainfall distribution in different areas. After collecting the data, first, the average number of rainfall months for each of the studied stations for a period was obtained. Then the data were arranged numerically in the order of Gregorian months. Ultra-innovative artificial intelligence methods were used to perform spatial–temporal analysis. The results show that in autumn and winter all three factors were influential on rainfall in the region. The equation method of regression line trend was used to express the changes in rainfall in the study period, and it was concluded that during this period the rainfall trend in March, June, and December in all stations was decreasing. In May, all stations had an upward trend except for Harsin station. In other months, there are decreasing and increasing trends among the stations. The general trend for rainfall during the study period is also one of decreasing. Regarding the results, the standard deviation for the simulation is equal to 10.22 for autumn and 12.35% for winter. This value is about 17.97% and 7.19%, respectively, for the observed rainfall. The results show there are no significant differences between the model and measured data, so the artificial network is applicable for the simulated monthly precipitation.