气候变化领域集成服务门户(CCPortal): A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting

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DOI	10.1016/j.aej.2021.04.100
	A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting
	Ibrahim, Karim Sherif Mostafa Hassan; Huang, Yuk Feng; Ahmed, Ali Najah; Koo, Chai Hoon; El-Shafie, Ahmed
发表日期	2022
ISSN	1110-0168
EISSN	2090-2670
起始页码	279
结束页码	303
卷号	61 期号:1
英文摘要	Ever since the first introduction of Artificial Intelligence into the field of hydrology, it has further generated immense interest in researching aspects for further improvements to hydrology. This can be seen in the rising number of related works published. This culminated further with the combination of pioneering optimization techniques. Who would have thought that the birds and the bees can offer advances in the mathematical sciences and so have the ants too? The ingenuity of humans is spelled out in the algorithms that mimic many natural activities, like pack hunting by the wolves! This review paper serves to broadcast more of the intriguing interest in newfound procedures in optimal forecasting. Reservoirs are the main and most efficient water storage facilities for managing uneven water distribution. However, due to the major global climate changes which affect rainfall trend and weather, it has been a necessity to find an alternative solution for effective conventional water balance. A multifunctional reservoir operation appears to require the operator to make wise decisions to achieve an optimal reservoir operation. One of the most important aspects of all this is the forecasting of streamflows. For this, Artificial Intelligence (AI) seems to be the best alternative solution; as in the past three decades, there has been a drastic increase in building and developing AI models for forecasting and modelling unstable patterns in various hydrological fields. Nevertheless, AI models are also required to be optimized in tandem to achieve the best result, leading thus to the desirous forming of hybrid models between a standalone AI model and optimization techniques. This comprehensive study categorizes machine learning into three main categories, together with the optimization techniques, and will next explore the various AI model used for different hydrology fields along with the most common optimization techniques. Summarization of findings under every section is provided. Some advantages and disadvantages found through literature reviews are summarized for ease of reference. Finally, future recommendations and overall conclusions drawn from the results of researchers are included. This current review focuses on papers from high-impact factor publications based on 10 years starting from (2009 to 2020). (C) 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Alexandria University.
英文关键词	Artificial intelligence; Artificial Neural Network (ANN); Support Vector Machine (SVM); Adaptive Neuro-Fuzzy Inference System (ANFIS); Optimization Algorithms; Genetic Algorithms (GA); Particle Swarm Optimization (PSO); Artificial Bee Colony (ABC); Reservoir Inflows; Streamflow forecasting
语种	英语
WOS研究方向	Engineering, Multidisciplinary
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000709484000002
来源期刊	ALEXANDRIA ENGINEERING JOURNAL
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281130
作者单位	Universiti Malaya
推荐引用方式 GB/T 7714	Ibrahim, Karim Sherif Mostafa Hassan,Huang, Yuk Feng,Ahmed, Ali Najah,et al. A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting[J],2022,61(1).
APA	Ibrahim, Karim Sherif Mostafa Hassan,Huang, Yuk Feng,Ahmed, Ali Najah,Koo, Chai Hoon,&El-Shafie, Ahmed.(2022).A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting.ALEXANDRIA ENGINEERING JOURNAL,61(1).
MLA	Ibrahim, Karim Sherif Mostafa Hassan,et al."A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting".ALEXANDRIA ENGINEERING JOURNAL 61.1(2022).