A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting

Ibrahim, Karim Sherif Mostafa Hassan and Huang, Yuk Feng and Ahmed, Ali Najah and Koo, Chai Hoon and El-Shafie, Ahmed (2022) A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting. Alexandria Engineering Journal, 61 (1). pp. 279-303. ISSN 1110-0168, DOI https://doi.org/10.1016/j.aej.2021.04.1001110-0168.

Full text not available from this repository.


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.

Item Type: Article
Funders: Ministry of Education (MOE) through Fundamental Research Grant Scheme [Ministry of Education (MOE) through Fundamental Research Grant Scheme], Universiti Tunku Abdul Rahman [6235/H02]
Uncontrolled Keywords: 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
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Engineering > Department of Civil Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 17 Jul 2022 06:51
Last Modified: 17 Jul 2022 06:51
URI: http://eprints.um.edu.my/id/eprint/33551

Actions (login required)

View Item View Item