干旱区生态环境知识资源中心(Arid): Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques

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DOI	10.1007/s11269-022-03339-2
	Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques
	Tofiq, Yahia Mutalib; Latif, Sarmad Dashti; Ahmed, Ali Najah; Kumar, Pavitra; El-Shafie, Ahmed
通讯作者	Kumar, P
来源期刊	WATER RESOURCES MANAGEMENT
ISSN	0920-4741
EISSN	1573-1650
出版年	2022
卷号	36 期号:15 页码:5999-6016
英文摘要	The development of a river inflow prediction is a prerequisite for dam reservoir management. Precise forecasting leads to better irrigation water management, reservoir operation refinement, enhanced hydropower output and mitigation of risk of natural hazards such as flooding. Dam created reservoirs prove to be an essential source of water in arid and semi-arid regions. Over the years, Artificial Intelligence (AI) has been used for development of models for prediction of various natural variables in different engineering fields. Also, several AI models have been proved to be beneficial over the conventional models in efficient prediction of various natural variables. In this study, four AI models, namely, Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF) and Boosted Tree Regression (BTR) were developed and trained over 130-years of monthly historical rainfall data to forecast streamflow at Aswan High Dam, Egypt. The input parameters were selected according to the Autocorrelation Function (ACF) plot. The findings revealed that RF model outperformed other techniques and could provide precise monthly streamflow prediction with the lowest RMSE (2.2395) and maximum WI (0.998462), R-2 (0.9012). The input combination for the optimum RF model was Q(t-1), Q(t-11), and Q(t-12) (i.e., one-, eleven- and twelve-months delay inputs). The optimum RF model provides a reliable source of data for inflow predictions, which allows improved utilization of water resources and long-term water resource planning and management.
英文关键词	Streamflow prediction Aswan High Dam Artificial Neural Network Support Vector Machine Random Forest Boosted Tree Regression
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000866331400003
WOS关键词	SUPPORT VECTOR MACHINE ; NEURAL-NETWORKS ; PREDICTION ; REGRESSION ; BIVARIATE
WOS类目	Engineering, Civil ; Water Resources
WOS研究方向	Engineering ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394892
推荐引用方式 GB/T 7714	Tofiq, Yahia Mutalib,Latif, Sarmad Dashti,Ahmed, Ali Najah,et al. Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques[J],2022,36(15):5999-6016.
APA	Tofiq, Yahia Mutalib,Latif, Sarmad Dashti,Ahmed, Ali Najah,Kumar, Pavitra,&El-Shafie, Ahmed.(2022).Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques.WATER RESOURCES MANAGEMENT,36(15),5999-6016.
MLA	Tofiq, Yahia Mutalib,et al."Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques".WATER RESOURCES MANAGEMENT 36.15(2022):5999-6016.