干旱区生态环境知识资源中心(Arid): Developing an innovative machine learning model for rainfall prediction in a Semi-Arid region

Arid

DOI	10.2166/hydro.2024.014
	Developing an innovative machine learning model for rainfall prediction in a Semi-Arid region
	Latif, Sarmad Dashti; Mohammed, Dyar Othman; Jaafar, Alhassan
通讯作者	Latif, SD
来源期刊	JOURNAL OF HYDROINFORMATICS
ISSN	1464-7141
EISSN	1465-1734
出版年	2024
卷号	26 期号:4 页码:904-914
英文摘要	Due to global climate change, managing water resources is one of the most critical challenges for most countries in the world, especially in the Middle East. In the Kurdistan Region of Iraq (KRI), there is a good amount of precipitation, surface-water, and groundwater, but the main issue is mismanagement of those sources. Rainfall is one of the major sources of water resources in KRI. In order to manage the available water resources and prevent natural disasters such as floods and droughts, there is a need for reliable models for forecasting rainfall. The current study focuses on developing a hybrid model namely, seasonal autoregressive integrated moving average combined with an artificial neural network (SARIMA-ANN) for forecasting monthly rainfall at Sulaymaniyah City for the duration of 1938-2012. For comparison purposes, a conventional machine learning model, namely, artificial neural networks (ANN) has been applied on the same data. Two different statistical measurements, namely, root mean square error (RMSE) and coefficient of determination (R2) have been used to check the accuracy of the proposed models. According to the findings, SARIMA-ANN outperformed ANN with RMSE = 11.5, RMSE = 51.002, R-2= 0.98, R-2 = 0.43, respectively. The findings of the current study could contribute to sustainable development goal (SDG) 6.
英文关键词	hybrid model rainfall prediction SARIMA-ANN Sulaymaniyah City Sustainable development goal (SDG) 6 water resources management
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:001188590500001
WOS类目	Computer Science, Interdisciplinary Applications ; Engineering, Civil ; Environmental Sciences ; Water Resources
WOS研究方向	Computer Science ; Engineering ; 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/2XILL650/404537
推荐引用方式 GB/T 7714	Latif, Sarmad Dashti,Mohammed, Dyar Othman,Jaafar, Alhassan. Developing an innovative machine learning model for rainfall prediction in a Semi-Arid region[J],2024,26(4):904-914.
APA	Latif, Sarmad Dashti,Mohammed, Dyar Othman,&Jaafar, Alhassan.(2024).Developing an innovative machine learning model for rainfall prediction in a Semi-Arid region.JOURNAL OF HYDROINFORMATICS,26(4),904-914.
MLA	Latif, Sarmad Dashti,et al."Developing an innovative machine learning model for rainfall prediction in a Semi-Arid region".JOURNAL OF HYDROINFORMATICS 26.4(2024):904-914.