干旱区生态环境知识资源中心(Arid): Spatiotemporal Analysis of Long-Term Rainfall in Semi-Arid Area Using Artificial Intelligence Models (Case Study: Ilam Province, Iran)

Arid

DOI	10.3390/w15193521
	Spatiotemporal Analysis of Long-Term Rainfall in Semi-Arid Area Using Artificial Intelligence Models (Case Study: Ilam Province, Iran)
	Moradpoor, Navid; Najarchi, Mohsen; Hezave, Seyed Mohammad Mirhoseini
通讯作者	Najarchi, M
来源期刊	WATER
EISSN	2073-4441
出版年	2023
卷号	15 期号:19
英文摘要	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.
英文关键词	spatiotemporal analysis long-term rainfall Ilam city meta innovative artificial intelligence
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001086787700001
WOS类目	Environmental Sciences ; Water Resources
WOS研究方向	Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/399085
推荐引用方式 GB/T 7714	Moradpoor, Navid,Najarchi, Mohsen,Hezave, Seyed Mohammad Mirhoseini. Spatiotemporal Analysis of Long-Term Rainfall in Semi-Arid Area Using Artificial Intelligence Models (Case Study: Ilam Province, Iran)[J],2023,15(19).
APA	Moradpoor, Navid,Najarchi, Mohsen,&Hezave, Seyed Mohammad Mirhoseini.(2023).Spatiotemporal Analysis of Long-Term Rainfall in Semi-Arid Area Using Artificial Intelligence Models (Case Study: Ilam Province, Iran).WATER,15(19).
MLA	Moradpoor, Navid,et al."Spatiotemporal Analysis of Long-Term Rainfall in Semi-Arid Area Using Artificial Intelligence Models (Case Study: Ilam Province, Iran)".WATER 15.19(2023).