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Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization

Autocorrelation; Biomimetics; Forecasting; Multilayer neural networks; Particle swarm optimization (PSO); Rain; Runoff; Support vector machines; Swarm intelligence; Autocorrelation functions; Cross-correlation function; Hyper-parameter optimizations; Least squares support vector machines; Machine le...

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Main Authors: Tikhamarine Y., Souag-Gamane D., Ahmed A.N., Sammen S.S., Kisi O., Huang Y.F., El-Shafie A.
Other Authors: 57210575507
Format: Article
Published: Elsevier B.V. 2023
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spelling my.uniten.dspace-252222023-05-29T16:07:25Z Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization Tikhamarine Y. Souag-Gamane D. Ahmed A.N. Sammen S.S. Kisi O. Huang Y.F. El-Shafie A. 57210575507 55363629300 57214837520 57192093108 6507051085 55807263900 16068189400 Autocorrelation; Biomimetics; Forecasting; Multilayer neural networks; Particle swarm optimization (PSO); Rain; Runoff; Support vector machines; Swarm intelligence; Autocorrelation functions; Cross-correlation function; Hyper-parameter optimizations; Least squares support vector machines; Machine learning methods; Partial autocorrelation function; Rainfall - Runoff modelling; Rainfall-runoff relationship; Learning systems; accuracy assessment; autocorrelation; hydrological cycle; machine learning; numerical method; optimization; rainfall-runoff modeling; Parabuteo Rainfall and runoff are considered the main components in the hydrological cycle. Developing an accurate model to capture the dynamic connection between rainfall and runoff remains a problematic task for engineers. Several studies have been carried out to develop models to accurately predict the changes in runoff from rainfall. However, these models have limitations in terms of accuracy and complexity when large numbers of parameters are needed. Therefore, recently, with the advancement of data-driven techniques, a vast number of hydrologists have adopted models to predict changes in runoff. However, data-driven models still encounter several limitations related to hyperparameter optimization and overfitting. Hence, there is a need to improve these models to overcome these limitations. In this study, data-driven techniques such as a Multi-Layer Perceptron (MLP) neural network and Least Squares Support Vector Machine (LSSVM) are integrated with an advanced nature-inspired optimizer, namely, Harris Hawks Optimization (HHO) to model the rainfall-runoff relationship. Five different scenarios will be examined based on the autocorrelation function (ACF), cross-correlation function (CCF) and partial autocorrelation function (PACF). Finally, for comprehensive analysis, the performance of the proposed model will then be compared with integrated data-driven techniques with particle swarm optimization (PSO). The results revealed that all the augmented models with HHO outperformed other integrated models with PSO in predicting the changes in runoff. In addition, a high level of accuracy in predicting runoff values was achieved when HHO was integrated with LSSVM. � 2020 Elsevier B.V. Final 2023-05-29T08:07:25Z 2023-05-29T08:07:25Z 2020 Article 10.1016/j.jhydrol.2020.125133 2-s2.0-85086567198 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85086567198&doi=10.1016%2fj.jhydrol.2020.125133&partnerID=40&md5=0fcb898513bc743ab714a773a3cfd3d9 https://irepository.uniten.edu.my/handle/123456789/25222 589 125133 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 Autocorrelation; Biomimetics; Forecasting; Multilayer neural networks; Particle swarm optimization (PSO); Rain; Runoff; Support vector machines; Swarm intelligence; Autocorrelation functions; Cross-correlation function; Hyper-parameter optimizations; Least squares support vector machines; Machine learning methods; Partial autocorrelation function; Rainfall - Runoff modelling; Rainfall-runoff relationship; Learning systems; accuracy assessment; autocorrelation; hydrological cycle; machine learning; numerical method; optimization; rainfall-runoff modeling; Parabuteo
author2 57210575507
author_facet 57210575507
Tikhamarine Y.
Souag-Gamane D.
Ahmed A.N.
Sammen S.S.
Kisi O.
Huang Y.F.
El-Shafie A.
format Article
author Tikhamarine Y.
Souag-Gamane D.
Ahmed A.N.
Sammen S.S.
Kisi O.
Huang Y.F.
El-Shafie A.
spellingShingle Tikhamarine Y.
Souag-Gamane D.
Ahmed A.N.
Sammen S.S.
Kisi O.
Huang Y.F.
El-Shafie A.
Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization
author_sort Tikhamarine Y.
title Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization
title_short Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization
title_full Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization
title_fullStr Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization
title_full_unstemmed Rainfall-runoff modelling using improved machine learning methods: Harris hawks optimizer vs. particle swarm optimization
title_sort rainfall-runoff modelling using improved machine learning methods: harris hawks optimizer vs. particle swarm optimization
publisher Elsevier B.V.
publishDate 2023
_version_ 1806427961895682048
score 13.211869

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