Forecasting Muar river water quality using radial basis function neural network

Monitoring and analysis of river water quality is an important element in the environmental monitoring policy and management. Fishing, tourism, drinking and most importantly domestic usage require an acceptable level of river water quality. The modeling of complex and nonlinear systems like river is...

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Main Author:	Abd. Jalal, Mohd. Razi
Format:	Thesis
Language:	English
Published:	2013
Subjects:	QA76 Computer software
Online Access:	http://eprints.utm.my/id/eprint/42127/5/MohdRaziAbdJalalMFKE2013.pdf
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spelling	my-utm-ep.421272017-06-20T08:04:31Z Forecasting Muar river water quality using radial basis function neural network 2013-12 Abd. Jalal, Mohd. Razi QA76 Computer software Monitoring and analysis of river water quality is an important element in the environmental monitoring policy and management. Fishing, tourism, drinking and most importantly domestic usage require an acceptable level of river water quality. The modeling of complex and nonlinear systems like river is difficult due to the presence of many variables and disturbance. Usually, the dynamic of the problem is modeled using mathematical relationship. However, most of the time a model requires a lot of information and running its simulation needs a significant amount of time. This project attempts to avoid this process by approximating the problem using a type of Artificial Neural Networks (ANN), which is the Radial Basis Function Neural Networks (RBFNN) instead of commonly used ANN: the Multilayer Perceptron (MLP). RBFNN was assessed to forecast water quality in Muar River, Malaysia where historical and lagged data of water quality were used as input for the networks, and forecasting accuracy was evaluated by using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Correlation Coefficient (CC). It was found that the RBFNN could be used effectively to predict one-day ahead of turbidity and aluminium value of Muar River. The RBF network produced slightly better results in forecasting with lower value of RMSE; 0.0394 and MAE; 0.0208 but higher value of CC; 0.5385 compared to MLP network for value of RMSE; 0.0435, MAE; 0.0230 and CC; 0.5213 in aluminium forecasting. The same observations were also found in turbidity forecasting where RBF network for value of RMSE; 40.3812, MAE; 25.8489 and CC; 0.6821 slightly better than MLP network for value of RMSE; 40.5804, MAE; 26.9558 and CC; 0.6453. RBF network processing time proved to be 77.9% to 80.9% faster than MLP network in forecasting aluminium and turbidity. 2013-12 Thesis http://eprints.utm.my/id/eprint/42127/ http://eprints.utm.my/id/eprint/42127/5/MohdRaziAbdJalalMFKE2013.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Abd. Jalal, Mohd. Razi Forecasting Muar river water quality using radial basis function neural network
description	Monitoring and analysis of river water quality is an important element in the environmental monitoring policy and management. Fishing, tourism, drinking and most importantly domestic usage require an acceptable level of river water quality. The modeling of complex and nonlinear systems like river is difficult due to the presence of many variables and disturbance. Usually, the dynamic of the problem is modeled using mathematical relationship. However, most of the time a model requires a lot of information and running its simulation needs a significant amount of time. This project attempts to avoid this process by approximating the problem using a type of Artificial Neural Networks (ANN), which is the Radial Basis Function Neural Networks (RBFNN) instead of commonly used ANN: the Multilayer Perceptron (MLP). RBFNN was assessed to forecast water quality in Muar River, Malaysia where historical and lagged data of water quality were used as input for the networks, and forecasting accuracy was evaluated by using Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and Correlation Coefficient (CC). It was found that the RBFNN could be used effectively to predict one-day ahead of turbidity and aluminium value of Muar River. The RBF network produced slightly better results in forecasting with lower value of RMSE; 0.0394 and MAE; 0.0208 but higher value of CC; 0.5385 compared to MLP network for value of RMSE; 0.0435, MAE; 0.0230 and CC; 0.5213 in aluminium forecasting. The same observations were also found in turbidity forecasting where RBF network for value of RMSE; 40.3812, MAE; 25.8489 and CC; 0.6821 slightly better than MLP network for value of RMSE; 40.5804, MAE; 26.9558 and CC; 0.6453. RBF network processing time proved to be 77.9% to 80.9% faster than MLP network in forecasting aluminium and turbidity.
format	Thesis
qualification_level	Master's degree
author	Abd. Jalal, Mohd. Razi
author_facet	Abd. Jalal, Mohd. Razi
author_sort	Abd. Jalal, Mohd. Razi
title	Forecasting Muar river water quality using radial basis function neural network
title_short	Forecasting Muar river water quality using radial basis function neural network
title_full	Forecasting Muar river water quality using radial basis function neural network
title_fullStr	Forecasting Muar river water quality using radial basis function neural network
title_full_unstemmed	Forecasting Muar river water quality using radial basis function neural network
title_sort	forecasting muar river water quality using radial basis function neural network
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2013
url	http://eprints.utm.my/id/eprint/42127/5/MohdRaziAbdJalalMFKE2013.pdf
_version_	1747816696327962624

Forecasting Muar river water quality using radial basis function neural network

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