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Flood forecasting using committee machine with intelligent systems: a framework for advanced machine learning approach

Among many natural hazards, flood disasters are the most incisive, causing tremendous casualties, in-depth injury to human life, property losses and agriculture, therefore affected the socioeconomic system of the area. Contributing to disaster risk reduction and the property damage associated with f...

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Main Authors: Faruq, Amrul, Abdullah, Shahrum Shah, Marto, Aminaton, Che Razali, Che Munira, Mohd. Hussein, Shamsul Faisal
Format: Conference or Workshop Item
Language:English
Published: 2020
Subjects:
TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utm.my/id/eprint/93809/1/ShahrumShah2020_FloodForecastingusingCommitteeMachine.pdf
http://eprints.utm.my/id/eprint/93809/
http://dx.doi.org/10.1088/1755-1315/479/1/012039
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id my.utm.93809
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spelling my.utm.938092021-12-31T08:51:14Z http://eprints.utm.my/id/eprint/93809/ Flood forecasting using committee machine with intelligent systems: a framework for advanced machine learning approach Faruq, Amrul Abdullah, Shahrum Shah Marto, Aminaton Che Razali, Che Munira Mohd. Hussein, Shamsul Faisal TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Among many natural hazards, flood disasters are the most incisive, causing tremendous casualties, in-depth injury to human life, property losses and agriculture, therefore affected the socioeconomic system of the area. Contributing to disaster risk reduction and the property damage associated with floods, the research on the advancement of flood modelling and forecasting is increasingly essential. Flood forecasting technique is one of the most significant current discussion in hydrological-engineering area, in which a highly complex system and difficult to model. The past decade has been seen the rapid development of machine learning techniques contributed extremely within the advancement of prediction systems providing better performance and efficient solutions. This paper proposes a framework design of flood forecasting model utilizing committee machine learning methods. Previously published works employing committee machine techniques in the analysis of the robustness of the model, effectiveness, and accuracy are particularly investigated on the used in various subjects. It is found that artificial neural networks, hybridizations, and model optimization are reported as the most effective ways for the improved development of machine learning methods. The proposed framework employs four representative intelligent systems as individual members, including radial basis neural networks, adaptive-neuro fuzzy, support vector machine and deep learning networks to construct a committee machine. As a conclusion, this committee machine with intelligent systems appears to be capable of enhancing the designing of flood forecasting model for disaster risk reduction. 2020-07-13 Conference or Workshop Item PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/93809/1/ShahrumShah2020_FloodForecastingusingCommitteeMachine.pdf Faruq, Amrul and Abdullah, Shahrum Shah and Marto, Aminaton and Che Razali, Che Munira and Mohd. Hussein, Shamsul Faisal (2020) Flood forecasting using committee machine with intelligent systems: a framework for advanced machine learning approach. In: 7th AUN/SEED-Net Regional Conference On Natural Disaster 2019, RCND 2019, 25 November 2019 - 26 November 2019, Putrajaya, Malaysia. http://dx.doi.org/10.1088/1755-1315/479/1/012039
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Faruq, Amrul
Abdullah, Shahrum Shah
Marto, Aminaton
Che Razali, Che Munira
Mohd. Hussein, Shamsul Faisal
Flood forecasting using committee machine with intelligent systems: a framework for advanced machine learning approach
description Among many natural hazards, flood disasters are the most incisive, causing tremendous casualties, in-depth injury to human life, property losses and agriculture, therefore affected the socioeconomic system of the area. Contributing to disaster risk reduction and the property damage associated with floods, the research on the advancement of flood modelling and forecasting is increasingly essential. Flood forecasting technique is one of the most significant current discussion in hydrological-engineering area, in which a highly complex system and difficult to model. The past decade has been seen the rapid development of machine learning techniques contributed extremely within the advancement of prediction systems providing better performance and efficient solutions. This paper proposes a framework design of flood forecasting model utilizing committee machine learning methods. Previously published works employing committee machine techniques in the analysis of the robustness of the model, effectiveness, and accuracy are particularly investigated on the used in various subjects. It is found that artificial neural networks, hybridizations, and model optimization are reported as the most effective ways for the improved development of machine learning methods. The proposed framework employs four representative intelligent systems as individual members, including radial basis neural networks, adaptive-neuro fuzzy, support vector machine and deep learning networks to construct a committee machine. As a conclusion, this committee machine with intelligent systems appears to be capable of enhancing the designing of flood forecasting model for disaster risk reduction.
format Conference or Workshop Item
author Faruq, Amrul
Abdullah, Shahrum Shah
Marto, Aminaton
Che Razali, Che Munira
Mohd. Hussein, Shamsul Faisal
author_facet Faruq, Amrul
Abdullah, Shahrum Shah
Marto, Aminaton
Che Razali, Che Munira
Mohd. Hussein, Shamsul Faisal
author_sort Faruq, Amrul
title Flood forecasting using committee machine with intelligent systems: a framework for advanced machine learning approach
title_short Flood forecasting using committee machine with intelligent systems: a framework for advanced machine learning approach
title_full Flood forecasting using committee machine with intelligent systems: a framework for advanced machine learning approach
title_fullStr Flood forecasting using committee machine with intelligent systems: a framework for advanced machine learning approach
title_full_unstemmed Flood forecasting using committee machine with intelligent systems: a framework for advanced machine learning approach
title_sort flood forecasting using committee machine with intelligent systems: a framework for advanced machine learning approach
publishDate 2020
url http://eprints.utm.my/id/eprint/93809/1/ShahrumShah2020_FloodForecastingusingCommitteeMachine.pdf
http://eprints.utm.my/id/eprint/93809/
http://dx.doi.org/10.1088/1755-1315/479/1/012039
_version_ 1720980128077447168
score 13.211869

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