Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions
Article
Article Title | Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions |
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ERA Journal ID | 32032 |
Article Category | Article |
Authors | Tao, Hai, Abba, Sani I., Al-Areeq, Ahmed M., Tangang, Fredolin, Samantaray, Sandeep, Sahoo, Abinash, Siqueira, Hugo Valadares, Maroufpoor, Saman, Demir, Vahdettin, Bokde, Neeraj Dhanraj, Goliatt, Leonardo, Jamei, Mehdi, Ahmadianfar, Iman, Bhagat, Suraj Kumar, Halder, Bijay, Guo, Tianli, Helman, Daniel S., Ali, Mumtaz, Sattar, Sabaa, Al-Khafaji, Zainab and Yaseen, Zaher Mundher |
Journal Title | Engineering Applications of Artificial Intelligence |
Journal Citation | 129 |
Article Number | 107559 |
Number of Pages | 46 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0952-1976 |
1873-6769 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.engappai.2023.107559 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0952197623017438 |
Abstract | River flow (Qflow) is a hydrological process that considerably impacts the management and sustainability of water resources. The literature has shown great potential for nature-inspired optimized algorithms (NIOAs), like hybrid artificial intelligence (HAI) models, for Qflow modeling. Qflow forecasting needs to be accurate, robust, reliable, and capable of resolving complex non-linear problems to support the decision authority in local and national governments and NGOs. This extensive survey provides a literature review of 100-plus high-impact factor journal articles on developing NIOAs models during 2000–2022. This encompasses a comprehensive review of the established research in different climatic zones, NIOA types, artificial intelligence (AI) models, the input parameters used for model development, Qflow on different time scales, and model evaluation using a wide range of performance metrics. The review also assessed and evaluated several components of relevant literature, along with detailing the existing research gaps. Moreover, the global research gap with future direction is discussed based on current research limitations and possibilities. The data availability evaluation and futuristic suggestions are drafted logically. The review revealed the superiority of the NIOAs among all applied algorithms in the literature. Further, the review concludes that there is a need to improve technical aspects of Qflow forecasting and bridge the gap between scientific research, hydrometeorological model development, and real-world flood and drought management using probabilistic nature inspired (NI) forecasts, especially through effective communication. |
Keywords | Data availability ; Machine learning ; Nature-inspired algorithms ; River flow modeling ; Optimization algorithms |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 461103. Deep learning |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Qiannan Normal University for Nationalities, China |
INTI International University,Malaysia | |
King Fahd University of Petroleum and Minerals, Saudi Arabia | |
National University of Malaysia | |
National Institute of Technology Srinagar, India | |
Odisha University of Technology and Research, India | |
Federal University of Technology, Brazil | |
University of Tehran, Iran | |
KTO Karatay University, Turkiye | |
Aarhus University, Denmark | |
Federal University of Juiz de Fora, Brazil | |
Shahid Chamran University of Ahvaz, Iran | |
University of Prince Edward Island, Canada | |
Behbahan Khatam Alanbia University of Technology, Iran | |
Ton Duc Thang University, Vietnam | |
Al-Ayen University, Iraq | |
Northwest A&F University, China | |
College of Micronesia-FSM, Federated States of Micronesia | |
UniSQ College | |
Al-Turath University College, Iraq | |
Al-Mustaqbal University College, Iraq |
https://research.usq.edu.au/item/z3558/hybridized-artificial-intelligence-models-with-nature-inspired-algorithms-for-river-flow-modeling-a-comprehensive-review-assessment-and-possible-future-research-directions
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