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

Climate change; Fuzzy inference; Fuzzy neural networks; Fuzzy systems; Genetic algorithms; Hydrology; Particle swarm optimization (PSO); Reservoirs (water); Stream flow; Support vector machines; Water supply systems; Adaptive neuro-fuzzy inference system; Artificial bee colony; Artificial neural net...

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Main Authors: Ibrahim K.S.M.H., Huang Y.F., Ahmed A.N., Koo C.H., El-Shafie A.
Other Authors: 57225749816
Format: Review
Published: Elsevier B.V. 2023
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spelling my.uniten.dspace-273162023-05-29T17:42:36Z A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting Ibrahim K.S.M.H. Huang Y.F. Ahmed A.N. Koo C.H. El-Shafie A. 57225749816 55807263900 57214837520 57204843657 16068189400 Climate change; Fuzzy inference; Fuzzy neural networks; Fuzzy systems; Genetic algorithms; Hydrology; Particle swarm optimization (PSO); Reservoirs (water); Stream flow; Support vector machines; Water supply systems; Adaptive neuro-fuzzy inference system; Artificial bee colony; Artificial neural network; Genetic algorithm; Intelligence modeling; Optimization algorithms; Particle swarm optimization; Reservoir inflow; Streamflow forecasting; Support vector machine; Forecasting 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). � 2021 THE AUTHORS Final 2023-05-29T09:42:36Z 2023-05-29T09:42:36Z 2022 Review 10.1016/j.aej.2021.04.100 2-s2.0-85108512847 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85108512847&doi=10.1016%2fj.aej.2021.04.100&partnerID=40&md5=455f420af67fd62496670ab3d92f79ea https://irepository.uniten.edu.my/handle/123456789/27316 61 1 279 303 All Open Access, Gold Elsevier B.V. Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Climate change; Fuzzy inference; Fuzzy neural networks; Fuzzy systems; Genetic algorithms; Hydrology; Particle swarm optimization (PSO); Reservoirs (water); Stream flow; Support vector machines; Water supply systems; Adaptive neuro-fuzzy inference system; Artificial bee colony; Artificial neural network; Genetic algorithm; Intelligence modeling; Optimization algorithms; Particle swarm optimization; Reservoir inflow; Streamflow forecasting; Support vector machine; Forecasting
author2 57225749816
author_facet 57225749816
Ibrahim K.S.M.H.
Huang Y.F.
Ahmed A.N.
Koo C.H.
El-Shafie A.
format Review
author Ibrahim K.S.M.H.
Huang Y.F.
Ahmed A.N.
Koo C.H.
El-Shafie A.
spellingShingle Ibrahim K.S.M.H.
Huang Y.F.
Ahmed A.N.
Koo C.H.
El-Shafie A.
A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting
author_sort Ibrahim K.S.M.H.
title A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting
title_short A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting
title_full A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting
title_fullStr A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting
title_full_unstemmed A review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting
title_sort review of the hybrid artificial intelligence and optimization modelling of hydrological streamflow forecasting
publisher Elsevier B.V.
publishDate 2023
_version_ 1806427615624429568
score 13.214268

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