Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions

Hai Tao, Sani I. Abba, Ahmed M. Al-Areeq, Fredolin Tangang, Sandeep Samantaray, Abinash Sahoo, Hugo Valadares Siqueira, Saman Maroufpoor, Vahdettin Demir, Neeraj Dhanraj Bokde, Leonardo Goliatt, Mehdi Jamei, Iman Ahmadianfar, Suraj Kumar Bhagat, Bijay Halder, Tianli Guo, Daniel S. Helman, Mumtaz Ali, Sabaa Sattar, Zainab Al-KhafajiShamsuddin Shahid, Zaher Mundher Yaseen*

*Corresponding author for this work

Research output: Contribution to journalShort surveypeer-review

5 Scopus citations

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.

Original languageEnglish
Article number107559
JournalEngineering Applications of Artificial Intelligence
Volume129
DOIs
StatePublished - Mar 2024

Bibliographical note

Publisher Copyright:
© 2023 Elsevier Ltd

Keywords

  • Data availability
  • Machine learning
  • Nature-inspired algorithms
  • Optimization algorithms
  • River flow modeling

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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