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DOI	10.1109/ACCESS.2021.3092074
	Highly Accurate Prediction Model for Daily Runoff in Semi-Arid Basin Exploiting Metaheuristic Learning Algorithms
	Aoulmi, Yamina; Marouf, Nadir; Amireche, Mohamed; Kisi, Ozgur; Shubair, Raed M.; Keshtegar, Behrooz
通讯作者	Aoulmi, Y (corresponding author), Univ Larbi Ben Mhidi, Fac Technol, Dept Hydraul, Oum El Bouaghi 04000, Algeria.
来源期刊	IEEE ACCESS
ISSN	2169-3536
出版年	2021
卷号	9 页码:92500-92515
英文摘要	Developing trustworthy rainfall-runoff (R-R) models can offer serviceable information for planning and managing water resources. Use of artificial neural network (ANN) in adopting such models and predicting changes in runoff has become popular among many hydrologists from a long time. However, since the optimization is the most significant phase in ANN training, researchers' attentiveness has been attracted to the ANN's biggest problem, i.e. its susceptibility of being blocked in local minima. Consequently, use of genetic algorithms (GA), particle swarm optimization (PSO), firefly algorithm (FFA) and improved particle swarm optimization (IPSO) approaches to increase the performance of ANN, have gained remarkable interest among distinct modern heuristic optimization approaches. In this paper, the capability of four improved ANN methods, hybrid GA-based ANN, PSO-based ANN, FFA-based ANN and IPSO-based ANN in modeling rainfall-runoff (R-R) is investigated. IPSO has been used in order to increase the ability of PSO, where the new positions of particles are dynamically adjusted using two procedures which is given form the velocity obtained by PSO and proposed velocity in IPSO. The random normal grated number with a dynamical scale factor is used to compute the new position of the best particles in proposed velocity. Daily R-R data from six stations distributed in the Seybouse watershed located in semi-arid region in Algeria were used in models' development. The selection of the input data sets was carried out using the autocorrelation, partial autocorrelation and cross correlation functions. The results of the four hybrid models were compared via performance metrics, viz., Root Mean Square Error (RMSE), Pearson's correlation coefficient (R), Nash Sutcliffe Efficiency coefficient (NSE), and via graphical analysis (scatter plots, time series and Taylor diagram). Outcomes of the analysis at all study stations disclosed that all the ANN models enhanced with IPSO overachieved the GA-based ANN, PSO-based ANN and FFA-based ANN models in estimating runoff for both training and testing periods. The outcomes of the study indicate that the IPSO hybrid metaheuristic algorithm is the best technique in improving ANN capability in modeling daily R-R.
英文关键词	Predictive models Artificial neural networks Genetic algorithms Data models Training Computational modeling Optimization Rainfall-runoff models artificial neural network (ANN) genetic algorithms (GA) particle swarm optimization (PSO) firefly algorithm (FFA) improved particle swarm optimization (IPSO) Seybouse watershed semi-arid region
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:000673920800001
WOS关键词	ARTIFICIAL NEURAL-NETWORK ; PARTICLE SWARM OPTIMIZATION ; SUPPORT VECTOR REGRESSION ; FIREFLY ALGORITHM ; GENETIC ALGORITHM ; PSO ; FUZZY ; PERFORMANCE ; PARAMETERS ; BEHAVIOR
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS研究方向	Computer Science ; Engineering ; Telecommunications
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/363554
作者单位	[Aoulmi, Yamina; Marouf, Nadir; Amireche, Mohamed] Univ Larbi Ben Mhidi, Fac Technol, Dept Hydraul, Oum El Bouaghi 04000, Algeria; [Kisi, Ozgur] Ilia State Univ, Sch Technol, GE-0162 Tbilisi, Georgia; [Shubair, Raed M.] New York Univ Abu Dhabi NYU, Dept Elect & Comp Engn, Abu Dhabi, U Arab Emirates; [Keshtegar, Behrooz] Zabol Univ, Fac Engn, Dept Civil Engn, Zabol 3585698613, Iran
推荐引用方式 GB/T 7714	Aoulmi, Yamina,Marouf, Nadir,Amireche, Mohamed,et al. Highly Accurate Prediction Model for Daily Runoff in Semi-Arid Basin Exploiting Metaheuristic Learning Algorithms[J],2021,9:92500-92515.
APA	Aoulmi, Yamina,Marouf, Nadir,Amireche, Mohamed,Kisi, Ozgur,Shubair, Raed M.,&Keshtegar, Behrooz.(2021).Highly Accurate Prediction Model for Daily Runoff in Semi-Arid Basin Exploiting Metaheuristic Learning Algorithms.IEEE ACCESS,9,92500-92515.
MLA	Aoulmi, Yamina,et al."Highly Accurate Prediction Model for Daily Runoff in Semi-Arid Basin Exploiting Metaheuristic Learning Algorithms".IEEE ACCESS 9(2021):92500-92515.