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-Khafaji; Shamsuddin Shahid; Zaher Mundher Yaseen

doi:10.1016/j.engappai.2023.107559

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

Center for Quantitative Genetics and Genomics

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

59 Citations (Scopus)

Abstract

River flow (Q_flow) 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 Q_flow modeling. Q_flow 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, Q_flow 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 Q_flow 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 language	English
Article number	107559
Journal	Engineering Applications of Artificial Intelligence
Volume	129
Number of pages	46
ISSN	0952-1976
DOIs	https://doi.org/10.1016/j.engappai.2023.107559
Publication status	Published - Mar 2024

Keywords

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

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10.1016/j.engappai.2023.107559

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Tao, H., Abba, S. I., Al-Areeq, A. M., Tangang, F., Samantaray, S., Sahoo, A., Siqueira, H. V., Maroufpoor, S., Demir, V., Dhanraj Bokde, N., Goliatt, L., Jamei, M., Ahmadianfar, I., Bhagat, S. K., Halder, B., Guo, T., Helman, D. S., Ali, M., Sattar, S., ... Yaseen, Z. M. (2024). Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions. Engineering Applications of Artificial Intelligence, 129, Article 107559. https://doi.org/10.1016/j.engappai.2023.107559

@article{8b483b91042a434c96a011fd86890cb7,

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

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, Optimization algorithms, River flow modeling",

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

year = "2024",

month = mar,

doi = "10.1016/j.engappai.2023.107559",

language = "English",

volume = "129",

journal = "Engineering Applications of Artificial Intelligence",

issn = "0952-1976",

publisher = "Elsevier Ltd",

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Tao, H, Abba, SI, Al-Areeq, AM, Tangang, F, Samantaray, S, Sahoo, A, Siqueira, HV, Maroufpoor, S, Demir, V, Dhanraj Bokde, N, Goliatt, L, Jamei, M, Ahmadianfar, I, Bhagat, SK, Halder, B, Guo, T, Helman, DS, Ali, M, Sattar, S, Al-Khafaji, Z, Shahid, S & Yaseen, ZM 2024, 'Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions', Engineering Applications of Artificial Intelligence, vol. 129, 107559. https://doi.org/10.1016/j.engappai.2023.107559

Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research directions. / Tao, Hai; Abba, Sani I.; Al-Areeq, Ahmed M. et al.
In: Engineering Applications of Artificial Intelligence, Vol. 129, 107559, 03.2024.

Research output: Contribution to journal/Conference contribution in journal/Contribution to newspaper › Journal article › Research › peer-review

TY - JOUR

T1 - Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling

T2 - A comprehensive review, assessment, and possible future research directions

AU - Tao, Hai

AU - Abba, Sani I.

AU - Al-Areeq, Ahmed M.

AU - Tangang, Fredolin

AU - Samantaray, Sandeep

AU - Sahoo, Abinash

AU - Siqueira, Hugo Valadares

AU - Maroufpoor, Saman

AU - Demir, Vahdettin

AU - Dhanraj Bokde, Neeraj

AU - Goliatt, Leonardo

AU - Jamei, Mehdi

AU - Ahmadianfar, Iman

AU - Bhagat, Suraj Kumar

AU - Halder, Bijay

AU - Guo, Tianli

AU - Helman, Daniel S.

AU - Ali, Mumtaz

AU - Sattar, Sabaa

AU - Al-Khafaji, Zainab

AU - Shahid, Shamsuddin

AU - Yaseen, Zaher Mundher

PY - 2024/3

Y1 - 2024/3

N2 - 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.

AB - 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.

KW - Data availability

KW - Machine learning

KW - Nature-inspired algorithms

KW - Optimization algorithms

KW - River flow modeling

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U2 - 10.1016/j.engappai.2023.107559

DO - 10.1016/j.engappai.2023.107559

M3 - Journal article

AN - SCOPUS:85178647877

SN - 0952-1976

VL - 129

JO - Engineering Applications of Artificial Intelligence

JF - Engineering Applications of Artificial Intelligence

M1 - 107559

ER -

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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