Water level forecasting model using improved artificial neural network architecture

Reliable water level forecasting can help achieve efficient and optimum use of water resources and minimize flooding damage. Currently, artificial neural network (ANN) has been successfully tested in many forecasting studies, including river flow. However, the accuracy and reliability of the river f...

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Main Author:	Muhammad @ S. A. Khushren, Sulaiman
Format:	Thesis
Language:	English
Published:	2012
Subjects:	GB Physical geography
Online Access:	http://umpir.ump.edu.my/id/eprint/13483/ http://umpir.ump.edu.my/id/eprint/13483/ http://umpir.ump.edu.my/id/eprint/13483/1/MUHAMMAD%20%40%20S%20A%20KHUSHREN%20SULAIMAN.pdf

id	ump-13483
recordtype	eprints
spelling	ump-134832016-08-23T01:53:18Z http://umpir.ump.edu.my/id/eprint/13483/ Water level forecasting model using improved artificial neural network architecture Muhammad @ S. A. Khushren, Sulaiman GB Physical geography Reliable water level forecasting can help achieve efficient and optimum use of water resources and minimize flooding damage. Currently, artificial neural network (ANN) has been successfully tested in many forecasting studies, including river flow. However, the accuracy and reliability of the river flow forecasting in such application requires continuous research. In this context, this study aims at developing a high accuracy and reliable forecasting model using the ANN to predict high water level events. The aim is achieved by introducing four new approaches in the ANN modeling. Firstly, the internal architecture of ANN is enhanced by utilization of optimal steepness coefficient (OSC) in sigmoid function. Secondly, the external architecture of ANN is improved by applying zoning matching approach (ZMA) where data training selected is based on the target water level to be forecasted. Thirdly, model evaluation of forecasting results is improved by engineering approach where allowable offset errors are used to demonstrate the accuracy of forecasting results. Lastly, the correct prediction of up/down of water level is a new evaluation model to evaluate forecasting capability and reliability of the ANN model. A case study has been applied at the Rantau Panjang station at Johor River where hourly water level data dated from 1963 to 2008 have been examined to forecast daily and several hourly intervals lead-times. The result showed that the use of the OSC and ZMA techniques had not only improved the accuracy of ANN models compared to standard approach (SA) but also achieved high accuracy forecasting results. The allowable offset errors and up/down prediction measures have enriched measures in evaluating forecasting model's accuracy and reliability. The impact of the study is that the techniques can be the basis of future development approach in ANN based data forecasting model specially in monitoring flood events. 2012 Thesis NonPeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/13483/1/MUHAMMAD%20%40%20S%20A%20KHUSHREN%20SULAIMAN.pdf Muhammad @ S. A. Khushren, Sulaiman (2012) Water level forecasting model using improved artificial neural network architecture. PhD thesis, Universiti Malaysia Pahang. http://iportal.ump.edu.my/lib/item?id=chamo:83634&theme=UMP2
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	GB Physical geography
spellingShingle	GB Physical geography Muhammad @ S. A. Khushren, Sulaiman Water level forecasting model using improved artificial neural network architecture
description	Reliable water level forecasting can help achieve efficient and optimum use of water resources and minimize flooding damage. Currently, artificial neural network (ANN) has been successfully tested in many forecasting studies, including river flow. However, the accuracy and reliability of the river flow forecasting in such application requires continuous research. In this context, this study aims at developing a high accuracy and reliable forecasting model using the ANN to predict high water level events. The aim is achieved by introducing four new approaches in the ANN modeling. Firstly, the internal architecture of ANN is enhanced by utilization of optimal steepness coefficient (OSC) in sigmoid function. Secondly, the external architecture of ANN is improved by applying zoning matching approach (ZMA) where data training selected is based on the target water level to be forecasted. Thirdly, model evaluation of forecasting results is improved by engineering approach where allowable offset errors are used to demonstrate the accuracy of forecasting results. Lastly, the correct prediction of up/down of water level is a new evaluation model to evaluate forecasting capability and reliability of the ANN model. A case study has been applied at the Rantau Panjang station at Johor River where hourly water level data dated from 1963 to 2008 have been examined to forecast daily and several hourly intervals lead-times. The result showed that the use of the OSC and ZMA techniques had not only improved the accuracy of ANN models compared to standard approach (SA) but also achieved high accuracy forecasting results. The allowable offset errors and up/down prediction measures have enriched measures in evaluating forecasting model's accuracy and reliability. The impact of the study is that the techniques can be the basis of future development approach in ANN based data forecasting model specially in monitoring flood events.
format	Thesis
author	Muhammad @ S. A. Khushren, Sulaiman
author_facet	Muhammad @ S. A. Khushren, Sulaiman
author_sort	Muhammad @ S. A. Khushren, Sulaiman
title	Water level forecasting model using improved artificial neural network architecture
title_short	Water level forecasting model using improved artificial neural network architecture
title_full	Water level forecasting model using improved artificial neural network architecture
title_fullStr	Water level forecasting model using improved artificial neural network architecture
title_full_unstemmed	Water level forecasting model using improved artificial neural network architecture
title_sort	water level forecasting model using improved artificial neural network architecture
publishDate	2012
url	http://umpir.ump.edu.my/id/eprint/13483/ http://umpir.ump.edu.my/id/eprint/13483/ http://umpir.ump.edu.my/id/eprint/13483/1/MUHAMMAD%20%40%20S%20A%20KHUSHREN%20SULAIMAN.pdf
first_indexed	2023-09-18T22:16:11Z
last_indexed	2023-09-18T22:16:11Z
_version_	1777415352311873536

Water level forecasting model using improved artificial neural network architecture

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