Artificial Neural Network Flood Prediction for Sungai Isap Residence

A flood is an extremely dangerous disaster that can wipe away an entire city, coastline, and rural area. The flood can cause wide destrotion to property and life that has the supreme corrosive force and can be highly damaging. In order to decrease the damages caused by the flood, an Artificial Neura...

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Main Authors:	Khoo, Chun Keong, Mahfuzah, Mustafa, Ahmad Johari, Mohamad, M. H., Sulaiman, Nor Rul Hasma, Abdullah
Format:	Conference or Workshop Item
Language:	English English
Published:	2016
Subjects:	TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://umpir.ump.edu.my/id/eprint/16371/ http://umpir.ump.edu.my/id/eprint/16371/1/Artificial%20Neural%20Network%20Flood%20Prediction.pdf http://umpir.ump.edu.my/id/eprint/16371/7/fkee-2016-mahfuzah-artificial%20neural1.pdf

id	ump-16371
recordtype	eprints
spelling	ump-163712018-04-11T01:19:19Z http://umpir.ump.edu.my/id/eprint/16371/ Artificial Neural Network Flood Prediction for Sungai Isap Residence Khoo, Chun Keong Mahfuzah, Mustafa Ahmad Johari, Mohamad M. H., Sulaiman Nor Rul Hasma, Abdullah TK Electrical engineering. Electronics Nuclear engineering A flood is an extremely dangerous disaster that can wipe away an entire city, coastline, and rural area. The flood can cause wide destrotion to property and life that has the supreme corrosive force and can be highly damaging. In order to decrease the damages caused by the flood, an Artificial Neural Network (ANN) model has been established to predict flood in Sungai Isap, Kuantan, Pahang, Malaysia. This model is able to initiate the same brain thinking process and avoid the influence of the predict judgment. In this paper, presentation and comparison that using Bayesian Regularization (BR) back-propagation, Levenberg-Marquardt (LM) back-propagation and Gradient Descent (GD)back-propagation algorithms will be organized and carry out the result flood prediction. The predicted result of the Bayesian Regularization indicates a satisfactory performance. The conclusions also indicate that Bayesian Regularization is more versatile than Levenberg-Marquart and Gradient Descent with that can be backup or a practical tool for flood prediction. Temperature, precipitation, dew point, humidity, sea level pressure, visibility, wind, and river level data collected from January 2013 until May 2015 in the city of Sungai Isap, Kuantan is used for training, validation, and testing of the network model. The comparison is shown on the basis of mean square error (MSE) and regression (R). The prediction by training function Bayesian Regularization back-propagation found to be more suitable to predict flood model. 2016 Conference or Workshop Item PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/16371/1/Artificial%20Neural%20Network%20Flood%20Prediction.pdf application/pdf en http://umpir.ump.edu.my/id/eprint/16371/7/fkee-2016-mahfuzah-artificial%20neural1.pdf Khoo, Chun Keong and Mahfuzah, Mustafa and Ahmad Johari, Mohamad and M. H., Sulaiman and Nor Rul Hasma, Abdullah (2016) Artificial Neural Network Flood Prediction for Sungai Isap Residence. In: 2016 IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS2016)., 22 October 2016 , Shah Alam, Malaysia. pp. 1-6.. (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TK Electrical engineering. Electronics Nuclear engineering Khoo, Chun Keong Mahfuzah, Mustafa Ahmad Johari, Mohamad M. H., Sulaiman Nor Rul Hasma, Abdullah Artificial Neural Network Flood Prediction for Sungai Isap Residence
description	A flood is an extremely dangerous disaster that can wipe away an entire city, coastline, and rural area. The flood can cause wide destrotion to property and life that has the supreme corrosive force and can be highly damaging. In order to decrease the damages caused by the flood, an Artificial Neural Network (ANN) model has been established to predict flood in Sungai Isap, Kuantan, Pahang, Malaysia. This model is able to initiate the same brain thinking process and avoid the influence of the predict judgment. In this paper, presentation and comparison that using Bayesian Regularization (BR) back-propagation, Levenberg-Marquardt (LM) back-propagation and Gradient Descent (GD)back-propagation algorithms will be organized and carry out the result flood prediction. The predicted result of the Bayesian Regularization indicates a satisfactory performance. The conclusions also indicate that Bayesian Regularization is more versatile than Levenberg-Marquart and Gradient Descent with that can be backup or a practical tool for flood prediction. Temperature, precipitation, dew point, humidity, sea level pressure, visibility, wind, and river level data collected from January 2013 until May 2015 in the city of Sungai Isap, Kuantan is used for training, validation, and testing of the network model. The comparison is shown on the basis of mean square error (MSE) and regression (R). The prediction by training function Bayesian Regularization back-propagation found to be more suitable to predict flood model.
format	Conference or Workshop Item
author	Khoo, Chun Keong Mahfuzah, Mustafa Ahmad Johari, Mohamad M. H., Sulaiman Nor Rul Hasma, Abdullah
author_facet	Khoo, Chun Keong Mahfuzah, Mustafa Ahmad Johari, Mohamad M. H., Sulaiman Nor Rul Hasma, Abdullah
author_sort	Khoo, Chun Keong
title	Artificial Neural Network Flood Prediction for Sungai Isap Residence
title_short	Artificial Neural Network Flood Prediction for Sungai Isap Residence
title_full	Artificial Neural Network Flood Prediction for Sungai Isap Residence
title_fullStr	Artificial Neural Network Flood Prediction for Sungai Isap Residence
title_full_unstemmed	Artificial Neural Network Flood Prediction for Sungai Isap Residence
title_sort	artificial neural network flood prediction for sungai isap residence
publishDate	2016
url	http://umpir.ump.edu.my/id/eprint/16371/ http://umpir.ump.edu.my/id/eprint/16371/1/Artificial%20Neural%20Network%20Flood%20Prediction.pdf http://umpir.ump.edu.my/id/eprint/16371/7/fkee-2016-mahfuzah-artificial%20neural1.pdf
first_indexed	2023-09-18T22:21:59Z
last_indexed	2023-09-18T22:21:59Z
_version_	1777415717214224384

Artificial Neural Network Flood Prediction for Sungai Isap Residence

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