Flood frequency analysis at ungauged sites in Peninsular Malaysia using least square support vector machine

The ability of hierarchical cluster analysis (HCA) and least square support vector machine (LSSVM) in the estimation of flood quantiles at ungauged sites in Peninsular Malaysia were studied. Comparison between the multiple linear regression (MLR) models, LSSVM, HCA with MLR and HCA with LSSVM were p...

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Main Author: Roselan, Nur Shahidah
Format: Thesis
Published: 2014
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spelling my-utm-ep.484522017-08-08T01:41:35Z Flood frequency analysis at ungauged sites in Peninsular Malaysia using least square support vector machine 2014 Roselan, Nur Shahidah GB Physical geography The ability of hierarchical cluster analysis (HCA) and least square support vector machine (LSSVM) in the estimation of flood quantiles at ungauged sites in Peninsular Malaysia were studied. Comparison between the multiple linear regression (MLR) models, LSSVM, HCA with MLR and HCA with LSSVM were performed. To assess the effectiveness of this model, 70 catchments in the province of Peninsular Malaysia with five inputs variables which namely catchment area, longest drainage area, slope of mainstream, altitude of the mainstream and mean annual rainfall were used as case studies. The performance of HCA with LSSVM was compared with the MLR, HCA with MLR and LSSVM models using various statistical measures such as relative bias (RBIAS), mean absolute relative error (MARE) and mean squared relative error (MSRE). The results of the comparison indicate that the proposed model which is HCA with LSSVM has the lowest RBIAS, MARE and MSRE compared to the other three models. This model predicts flood quantile more accurately and provides a promising alternative technique in estimation of flood quantiles in ungauged sites 2014 Thesis http://eprints.utm.my/id/eprint/48452/ masters Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution Universiti Teknologi Malaysia
collection UTM Institutional Repository
topic GB Physical geography
spellingShingle GB Physical geography
Roselan, Nur Shahidah
Flood frequency analysis at ungauged sites in Peninsular Malaysia using least square support vector machine
description The ability of hierarchical cluster analysis (HCA) and least square support vector machine (LSSVM) in the estimation of flood quantiles at ungauged sites in Peninsular Malaysia were studied. Comparison between the multiple linear regression (MLR) models, LSSVM, HCA with MLR and HCA with LSSVM were performed. To assess the effectiveness of this model, 70 catchments in the province of Peninsular Malaysia with five inputs variables which namely catchment area, longest drainage area, slope of mainstream, altitude of the mainstream and mean annual rainfall were used as case studies. The performance of HCA with LSSVM was compared with the MLR, HCA with MLR and LSSVM models using various statistical measures such as relative bias (RBIAS), mean absolute relative error (MARE) and mean squared relative error (MSRE). The results of the comparison indicate that the proposed model which is HCA with LSSVM has the lowest RBIAS, MARE and MSRE compared to the other three models. This model predicts flood quantile more accurately and provides a promising alternative technique in estimation of flood quantiles in ungauged sites
format Thesis
qualification_level Master's degree
author Roselan, Nur Shahidah
author_facet Roselan, Nur Shahidah
author_sort Roselan, Nur Shahidah
title Flood frequency analysis at ungauged sites in Peninsular Malaysia using least square support vector machine
title_short Flood frequency analysis at ungauged sites in Peninsular Malaysia using least square support vector machine
title_full Flood frequency analysis at ungauged sites in Peninsular Malaysia using least square support vector machine
title_fullStr Flood frequency analysis at ungauged sites in Peninsular Malaysia using least square support vector machine
title_full_unstemmed Flood frequency analysis at ungauged sites in Peninsular Malaysia using least square support vector machine
title_sort flood frequency analysis at ungauged sites in peninsular malaysia using least square support vector machine
granting_institution Universiti Teknologi Malaysia, Faculty of Science
granting_department Faculty of Science
publishDate 2014
_version_ 1747817393737957376