干旱区生态环境知识资源中心(Arid): Comparison of a data-based model and a soil erosion model coupled with multiple linear regression for the prediction of reservoir sedimentation in a semi-arid environment

Arid

DOI	10.1007/s41207-020-00205-8
	Comparison of a data-based model and a soil erosion model coupled with multiple linear regression for the prediction of reservoir sedimentation in a semi-arid environment
	EL Bilali, Ali; Taleb, Abdeslam; EL Idrissi, Bouchaib; Brouziyne, Youssef; Mazigh, Nouhaila
通讯作者	EL Bilali, A
来源期刊	EURO-MEDITERRANEAN JOURNAL FOR ENVIRONMENTAL INTEGRATION
ISSN	2365-6433
EISSN	2365-7448
出版年	2020
卷号	5 期号:3
英文摘要	Reservoir sedimentation is a crucial challenge in planning and managing sustainable surface water resources in arid and semi-arid regions and must be assessed with accuracy. Both data-based models and conceptual models can be valuable tools for predicting reservoir sedimentation. In this study, we used an artificial neural network (ANN) approach and a modified Universal Soil Loss Equation coupled with multiple linear regression (MUSLE-MLR) model to predict yearly sedimentation in the Sidi Mohammed Ben Abdellah reservoir, located in a semi-arid region of Morocco. To construct the MUSLE-MLR model, we first calibrated and validated the MUSLE on 32 storms at four gauging stations upstream of the dam to estimate the sediment yield at these four gauging stations; we then developed the MLR model for combining sediment yield and reservoir sedimentation. The results of this model were then compared with the performance of the ANN model that was trained and validated over the periods 1975-2008 and 2009-2015, respectively. The comparison revealed that the calibrated MUSLE model is fairly useful to predict sediment yield at the watershed level. However, comparison of the two models during the validation process showed that the ANN (R(2)0.91, Nash-Sutcliffe Efficiency [NSE] 0.820) is more accurate and more suitable than the MUSLE-MLR model (R(2)0.819,NSE- 1.592) to predict reservoir sediment in the Sidi Mohammed Ben Abdellah reservoir. The findings of this study contribute to the armamentarium of potential tools that can be used to predict and manage reservoir sedimentation at the watershed and reservoir levels in a semi-arid context.
英文关键词	Reservoir Sedimentation Soil erosion Conceptual-based model Artificial neural network
类型	Article
语种	英语
收录类别	ESCI
WOS记录号	WOS:000578079200001
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; BATHYMETRIC SURVEY ; SWAT ; ANN ; SIMULATION ; MANAGEMENT ; REGION
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/334663
作者单位	[EL Bilali, Ali; Taleb, Abdeslam; Mazigh, Nouhaila] Hassan II Univ Casablanca, Fac Sci & Tech, Mohammadia, Morocco; [EL Idrissi, Bouchaib] Univ Quebec Trois Rivieres, Inst Innovat Ecomat Ecoprod & Eco Energies, Base Biomasse, Trois Rivieres, PQ, Canada; [Brouziyne, Youssef] Mohammed VI Polytech Univ UM6P, Int Water Res Inst, Benguerir, Morocco
推荐引用方式 GB/T 7714	EL Bilali, Ali,Taleb, Abdeslam,EL Idrissi, Bouchaib,et al. Comparison of a data-based model and a soil erosion model coupled with multiple linear regression for the prediction of reservoir sedimentation in a semi-arid environment[J],2020,5(3).
APA	EL Bilali, Ali,Taleb, Abdeslam,EL Idrissi, Bouchaib,Brouziyne, Youssef,&Mazigh, Nouhaila.(2020).Comparison of a data-based model and a soil erosion model coupled with multiple linear regression for the prediction of reservoir sedimentation in a semi-arid environment.EURO-MEDITERRANEAN JOURNAL FOR ENVIRONMENTAL INTEGRATION,5(3).
MLA	EL Bilali, Ali,et al."Comparison of a data-based model and a soil erosion model coupled with multiple linear regression for the prediction of reservoir sedimentation in a semi-arid environment".EURO-MEDITERRANEAN JOURNAL FOR ENVIRONMENTAL INTEGRATION 5.3(2020).