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Daily River Flow Forecasting with Hybrid Support Vector Machine - Particle Swarm Optimization

Atmospheric movements; Catchments; Flood control; Forecasting; Particle swarm optimization (PSO); Rain; Rivers; Stream flow; Sustainable development; Water management; Artificial intelligence techniques; Cameron highlands; Flood prevention; Hybrid model; Hybrid support vector machines; Meteorologica...

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Main Authors: Zaini N., Malek M.A., Yusoff M., Mardi N.H., Norhisham S.
Other Authors: 56905328500
Format: Conference Paper
Published: Institute of Physics Publishing 2023
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spelling my.uniten.dspace-238402023-05-29T14:52:17Z Daily River Flow Forecasting with Hybrid Support Vector Machine - Particle Swarm Optimization Zaini N. Malek M.A. Yusoff M. Mardi N.H. Norhisham S. 56905328500 55636320055 23391662400 57190171141 54581400300 Atmospheric movements; Catchments; Flood control; Forecasting; Particle swarm optimization (PSO); Rain; Rivers; Stream flow; Sustainable development; Water management; Artificial intelligence techniques; Cameron highlands; Flood prevention; Hybrid model; Hybrid support vector machines; Meteorological parameters; Model performance; River flow forecasting; Support vector machines The application of artificial intelligence techniques for river flow forecasting can further improve the management of water resources and flood prevention. This study concerns the development of support vector machine (SVM) based model and its hybridization with particle swarm optimization (PSO) to forecast short term daily river flow at Upper Bertam Catchment located in Cameron Highland, Malaysia. Ten years duration of historical rainfall, antecedent river flow data and various meteorology parameters data from 2003 to 2012 are used in this study. Four SVM based models are proposed which are SVM1, SVM2, SVM-PSO1 and SVM-PSO2 to forecast 1 to 7 day ahead of river flow. SVM1 and SVM-PSO1 are the models with historical rainfall and antecedent river flow as its input, while SVM2 and SVM-PSO2 are the models with historical rainfall, antecedent river flow data and additional meteorological parameters as input. The performances of the proposed model are measured in term of RMSE and R2 . It is found that, SVM2 outperformed SVM1 and SVM-PSO2 outperformed SVM-PSO1 which meant the additional meteorology parameters used as input to the proposed models significantly affect the model performances. Hybrid models SVM-PSO1 and SVM-PSO2 yield higher performances as compared to SVM1 and SVM2. It is found that hybrid models are more effective in forecasting river flow at 1 to 7 day ahead at the study area. � 2018 Published under licence by IOP Publishing Ltd. Final 2023-05-29T06:52:17Z 2023-05-29T06:52:17Z 2018 Conference Paper 10.1088/1755-1315/140/1/012035 2-s2.0-85046109729 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85046109729&doi=10.1088%2f1755-1315%2f140%2f1%2f012035&partnerID=40&md5=6fdd00456876b6dd9439e9abbb017925 https://irepository.uniten.edu.my/handle/123456789/23840 140 1 12035 All Open Access, Bronze Institute of Physics Publishing 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 Atmospheric movements; Catchments; Flood control; Forecasting; Particle swarm optimization (PSO); Rain; Rivers; Stream flow; Sustainable development; Water management; Artificial intelligence techniques; Cameron highlands; Flood prevention; Hybrid model; Hybrid support vector machines; Meteorological parameters; Model performance; River flow forecasting; Support vector machines
author2 56905328500
author_facet 56905328500
Zaini N.
Malek M.A.
Yusoff M.
Mardi N.H.
Norhisham S.
format Conference Paper
author Zaini N.
Malek M.A.
Yusoff M.
Mardi N.H.
Norhisham S.
spellingShingle Zaini N.
Malek M.A.
Yusoff M.
Mardi N.H.
Norhisham S.
Daily River Flow Forecasting with Hybrid Support Vector Machine - Particle Swarm Optimization
author_sort Zaini N.
title Daily River Flow Forecasting with Hybrid Support Vector Machine - Particle Swarm Optimization
title_short Daily River Flow Forecasting with Hybrid Support Vector Machine - Particle Swarm Optimization
title_full Daily River Flow Forecasting with Hybrid Support Vector Machine - Particle Swarm Optimization
title_fullStr Daily River Flow Forecasting with Hybrid Support Vector Machine - Particle Swarm Optimization
title_full_unstemmed Daily River Flow Forecasting with Hybrid Support Vector Machine - Particle Swarm Optimization
title_sort daily river flow forecasting with hybrid support vector machine - particle swarm optimization
publisher Institute of Physics Publishing
publishDate 2023
_version_ 1806426072040865792
score 13.19449

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