气候变化领域集成服务门户(CCPortal): The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction

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DOI	10.1016/j.asoc.2022.109739
	The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction
	Ikram, Rana Muhammad Adnan; Ewees, Ahmed A.; Parmar, Kulwinder Singh; Yaseen, Zaher Mundher; Shahid, Shamsuddin; Kisi, Ozgur
发表日期	2022
ISSN	1568-4946
EISSN	1872-9681
卷号	131
英文摘要	Precise streamflow prediction is necessary for better planning and managing available water and future water resources, especially for high altitude mountainous glacier melting affected basins in the climate change context. In the current study, a novel hybridized machine learning method, extended marine predators algorithm (EMPA)-based ANN (ANN-EMPA), is developed for streamflow estimation in the Upper Indus Basin, a key mountainous glacier melt affected basin of Pakistan. The prediction accuracy of the novel metaheuristic algorithm (EMPA) was also compared with several benchmark metaheuristic algorithms, including the marine predators algorithm (MPA), particle swarm optimization (PSO), genetic algorithm (GA), and grey wolf optimization (GWO). The results revealed that the newly developed hybridized ANN-EMPA outperformed the other hybrid ANN methods in streamflow prediction. ANN-EMPA improved the root mean square error, mean absolute error and Nash-Sutcliffe efficiency of ANN-PSO by 4.8, 4.1 and 0.5%, ANN-GA by 6.2, 5.6 and 0.6%, ANN-GWO by 3.7, 4.4 and 0.5%, and ANN-MPA by 3.2, 7.5 and 0.3%, respectively. Month number (MN) was also examined as input to the best models to assess its impact on the prediction precision. Obtained results showed that MN generally slightly improved the models' accuracy. Results also showed that temperature-based inputs provided better prediction accuracy than only streamflow as inputs. Therefore, the ANN-EMPA model can be used for streamflow estimation from temperature data only when long-term streamflow data is unavailable.(c) 2022 Elsevier B.V. All rights reserved.
英文关键词	Streamflow forecasting; Extended marine predators algorithm; Artificial neural networks; Optimization methods
语种	英语
WOS研究方向	Computer Science, Artificial Intelligence ; Computer Science, Interdisciplinary Applications
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000895417000001
来源期刊	APPLIED SOFT COMPUTING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281081
作者单位	Guangzhou University; Egyptian Knowledge Bank (EKB); Damietta University; I. K. Gujral Punjab Technical University; Universiti Teknologi Malaysia; Ilia State University; King Fahd University of Petroleum & Minerals
推荐引用方式 GB/T 7714	Ikram, Rana Muhammad Adnan,Ewees, Ahmed A.,Parmar, Kulwinder Singh,et al. The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction[J],2022,131.
APA	Ikram, Rana Muhammad Adnan,Ewees, Ahmed A.,Parmar, Kulwinder Singh,Yaseen, Zaher Mundher,Shahid, Shamsuddin,&Kisi, Ozgur.(2022).The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction.APPLIED SOFT COMPUTING,131.
MLA	Ikram, Rana Muhammad Adnan,et al."The viability of extended marine predators algorithm-based artificial neural networks for streamflow prediction".APPLIED SOFT COMPUTING 131(2022).