干旱区生态环境知识资源中心(Arid): Monthly inflow forecasting utilizing advanced artificial intelligence methods: a case study of Haditha Dam in Iraq

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DOI	10.1007/s00477-021-02052-7
	Monthly inflow forecasting utilizing advanced artificial intelligence methods: a case study of Haditha Dam in Iraq
	Allawi, Mohammed Falah; Hussain, Intesar Razaq; Salman, Majid Ibrahim; El-Shafie, Ahmed
通讯作者	Allawi, MF (corresponding author), Al Ayen Univ, New Era & Dev Civil Engn Res Grp, Sci Res Ctr, Thi Qar 64001, Iraq. ; Allawi, MF (corresponding author), Minist Water Resources, State Commiss Dams & Reservoirs, Baghdad, Iraq.
来源期刊	STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
ISSN	1436-3240
EISSN	1436-3259
出版年	2021
英文摘要	Accuracy of reservoir inflow forecasting is an important issue for the reservoir operation and water resources management. The main aim of the current study is to develop reliable models to forecast monthly inflow data. The present research proposed a robust model called co-active neuro-fuzzy inference system (CANFIS) to improve the forecasting accuracy. The reliability of the CANFIS model was evaluated by comparing with two different AI-based models, ANN and ANFIS model. To obtain the best forecasting result, the proposed models were trained utilizing four different Training Procedures. This study was conducted to forecast the inflow data for Haditha Dam on Euphrates River, Iraq. The comparison of models reveals that the CANFIS model is better than ANN and ANFIS model. The results showed that the second training procedure is more suitable for the forecasting models. The CANFIS model yielded a relative error of less than (15%), a low MAE (69.66 m(3)/s), a RMSE (78.10 m(3)/s) and a high correlation between the actual and forecasted data (R-2 = 0.97).
英文关键词	Inflow forecasting Semi-arid region Artificial intelligence models Data splitting
类型	Article ; Early Access
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000665807700001
WOS关键词	FUZZY INFERENCE SYSTEM ; ADAPTIVE NEURO-FUZZY ; NETWORK MODEL ; MULTI-LEAD ; EVAPORATION ; SIMULATION ; STREAMFLOW ; RAINFALL ; MACHINE ; RIVER
WOS类目	Engineering, Environmental ; Engineering, Civil ; Environmental Sciences ; Statistics & Probability ; Water Resources
WOS研究方向	Engineering ; Environmental Sciences & Ecology ; Mathematics ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/352459
作者单位	[Allawi, Mohammed Falah] Al Ayen Univ, New Era & Dev Civil Engn Res Grp, Sci Res Ctr, Thi Qar 64001, Iraq; [Allawi, Mohammed Falah; Hussain, Intesar Razaq; Salman, Majid Ibrahim] Minist Water Resources, State Commiss Dams & Reservoirs, Baghdad, Iraq; [El-Shafie, Ahmed] Univ Malaya, Civil Engn Dept, Fac Engn, Kuala Lumpur 50603, Malaysia
推荐引用方式 GB/T 7714	Allawi, Mohammed Falah,Hussain, Intesar Razaq,Salman, Majid Ibrahim,et al. Monthly inflow forecasting utilizing advanced artificial intelligence methods: a case study of Haditha Dam in Iraq[J],2021.
APA	Allawi, Mohammed Falah,Hussain, Intesar Razaq,Salman, Majid Ibrahim,&El-Shafie, Ahmed.(2021).Monthly inflow forecasting utilizing advanced artificial intelligence methods: a case study of Haditha Dam in Iraq.STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT.
MLA	Allawi, Mohammed Falah,et al."Monthly inflow forecasting utilizing advanced artificial intelligence methods: a case study of Haditha Dam in Iraq".STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT (2021).