Flood modelling using an integrated Artificial Neural Network and Neuro-Fuzzy technique for Johor River Basin, Malaysia

Flooding is one of the most destructive natural hazards that cause damage to both life and property every year, and therefore the development of flood model to determine inundation area in watersheds is important for decision makers. In recent years, data mining approaches such as artificial neural...

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Main Author:	Kia, Masoud Bakhtyari
Format:	Thesis
Language:	English
Published:	2013
Subjects:	Flow visualization Detectors Water resources development - Malaysia
Online Access:	http://psasir.upm.edu.my/id/eprint/56170/1/FK%202013%20113RR.pdf
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spelling	my-upm-ir.561702017-07-20T11:31:07Z Flood modelling using an integrated Artificial Neural Network and Neuro-Fuzzy technique for Johor River Basin, Malaysia 2013-08 Kia, Masoud Bakhtyari Flooding is one of the most destructive natural hazards that cause damage to both life and property every year, and therefore the development of flood model to determine inundation area in watersheds is important for decision makers. In recent years, data mining approaches such as artificial neural network (ANN) and Neuro-Fuzzy techniques are being increasingly used for flood modeling. Previously, these methods were frequently used for hydrological and flood modeling by taking rainfall as input and runoff data as output, usually without taking into consideration of other flood causative factors. The specific objective of this study is to develop a flood model using various flood causative factors by Multilayer Perceptron neural network MLP) and Local Linear Model Tree (LOLIMOT) techniques, and geographic information system (GIS) to modeling and simulate flood-prone areas in the southern part of Peninsular Malaysia. The ANN and Neuro-Fuzzy models for this study were developed in MATLAB using seven flood causative factors. Relevant thematic layers (including rainfall, slope, elevation, flow accumulation, soil, land use, and geology) are generated using GIS, and field surveys. In the context of objective weight assignments, the ANN is used to directly produce water levels and then the flood map is constructed in GIS. Comparison between the forecasted and observed river flow indicate that the accuracy of models are quite good especially in ANN model. The flood inundation area is derived based on this model by using DEM map. To measure the performance of the model, four criteria performances, including coefficient of determination (R2), the sum squared error, the mean square error, and the root mean square error are used. The verification results showed satisfactory agreement between the predicted and the real hydrological records. The sensitivity analysis performed shows that with the exception of the rainfall factor as the main reason of floods, the elevation is the most important factor and geology has the least influence on river flow. The study is first attempt to use these integration methods in the flood modeling that used different causative factors. The results of this study could be used to help local and national government plan for the future and develop appropriate (to the local environmental conditions) new infrastructure to protect the lives and property of the people of Johor. Flow visualization Detectors Water resources development - Malaysia 2013-08 Thesis http://psasir.upm.edu.my/id/eprint/56170/ http://psasir.upm.edu.my/id/eprint/56170/1/FK%202013%20113RR.pdf application/pdf en public phd doctoral Universiti Putra Malaysia Flow visualization Detectors Water resources development - Malaysia
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Flow visualization Detectors Water resources development - Malaysia
spellingShingle	Flow visualization Detectors Water resources development - Malaysia Kia, Masoud Bakhtyari Flood modelling using an integrated Artificial Neural Network and Neuro-Fuzzy technique for Johor River Basin, Malaysia
description	Flooding is one of the most destructive natural hazards that cause damage to both life and property every year, and therefore the development of flood model to determine inundation area in watersheds is important for decision makers. In recent years, data mining approaches such as artificial neural network (ANN) and Neuro-Fuzzy techniques are being increasingly used for flood modeling. Previously, these methods were frequently used for hydrological and flood modeling by taking rainfall as input and runoff data as output, usually without taking into consideration of other flood causative factors. The specific objective of this study is to develop a flood model using various flood causative factors by Multilayer Perceptron neural network MLP) and Local Linear Model Tree (LOLIMOT) techniques, and geographic information system (GIS) to modeling and simulate flood-prone areas in the southern part of Peninsular Malaysia. The ANN and Neuro-Fuzzy models for this study were developed in MATLAB using seven flood causative factors. Relevant thematic layers (including rainfall, slope, elevation, flow accumulation, soil, land use, and geology) are generated using GIS, and field surveys. In the context of objective weight assignments, the ANN is used to directly produce water levels and then the flood map is constructed in GIS. Comparison between the forecasted and observed river flow indicate that the accuracy of models are quite good especially in ANN model. The flood inundation area is derived based on this model by using DEM map. To measure the performance of the model, four criteria performances, including coefficient of determination (R2), the sum squared error, the mean square error, and the root mean square error are used. The verification results showed satisfactory agreement between the predicted and the real hydrological records. The sensitivity analysis performed shows that with the exception of the rainfall factor as the main reason of floods, the elevation is the most important factor and geology has the least influence on river flow. The study is first attempt to use these integration methods in the flood modeling that used different causative factors. The results of this study could be used to help local and national government plan for the future and develop appropriate (to the local environmental conditions) new infrastructure to protect the lives and property of the people of Johor.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Kia, Masoud Bakhtyari
author_facet	Kia, Masoud Bakhtyari
author_sort	Kia, Masoud Bakhtyari
title	Flood modelling using an integrated Artificial Neural Network and Neuro-Fuzzy technique for Johor River Basin, Malaysia
title_short	Flood modelling using an integrated Artificial Neural Network and Neuro-Fuzzy technique for Johor River Basin, Malaysia
title_full	Flood modelling using an integrated Artificial Neural Network and Neuro-Fuzzy technique for Johor River Basin, Malaysia
title_fullStr	Flood modelling using an integrated Artificial Neural Network and Neuro-Fuzzy technique for Johor River Basin, Malaysia
title_full_unstemmed	Flood modelling using an integrated Artificial Neural Network and Neuro-Fuzzy technique for Johor River Basin, Malaysia
title_sort	flood modelling using an integrated artificial neural network and neuro-fuzzy technique for johor river basin, malaysia
granting_institution	Universiti Putra Malaysia
publishDate	2013
url	http://psasir.upm.edu.my/id/eprint/56170/1/FK%202013%20113RR.pdf
_version_	1747812120823595008

Flood modelling using an integrated Artificial Neural Network and Neuro-Fuzzy technique for Johor River Basin, Malaysia

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