Forecasting model for the change of reservoir water level stage based on temporal pattern of reservoir water level

Reservoir water level forecasting is vital in reservoir operation and management.The output of the forecasting model can be used in reservoir decision support systems.This study demonstrates the application of Artificial Neural Network (ANN) in developing the forecasting model for the change of rese...

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Main Authors: Ashaary, Nur Athirah, Wan Ishak, Wan Hussain, Ku-Mahamud, Ku Ruhana
Format: Conference or Workshop Item
Language:English
Published: 2015
Subjects:
Online Access:https://repo.uum.edu.my/id/eprint/15657/1/PID203.pdf
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author Ashaary, Nur Athirah
Wan Ishak, Wan Hussain
Ku-Mahamud, Ku Ruhana
author_facet Ashaary, Nur Athirah
Wan Ishak, Wan Hussain
Ku-Mahamud, Ku Ruhana
author_sort Ashaary, Nur Athirah
collection UUM
description Reservoir water level forecasting is vital in reservoir operation and management.The output of the forecasting model can be used in reservoir decision support systems.This study demonstrates the application of Artificial Neural Network (ANN) in developing the forecasting model for the change of reservoir water level stage.In this study, sliding window technique has been used to extract the temporal pattern that represents time delays in the reservoir water level. The patterns are used as input to the ANN model.The results show that a model with 4 days of time delay has produced the acceptable performance with both low error rate and high accuracy.
first_indexed 2024-07-04T05:59:19Z
format Conference or Workshop Item
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institution Universiti Utara Malaysia
language English
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spelling uum-156572016-04-27T01:26:04Z https://repo.uum.edu.my/id/eprint/15657/ Forecasting model for the change of reservoir water level stage based on temporal pattern of reservoir water level Ashaary, Nur Athirah Wan Ishak, Wan Hussain Ku-Mahamud, Ku Ruhana QA75 Electronic computers. Computer science Reservoir water level forecasting is vital in reservoir operation and management.The output of the forecasting model can be used in reservoir decision support systems.This study demonstrates the application of Artificial Neural Network (ANN) in developing the forecasting model for the change of reservoir water level stage.In this study, sliding window technique has been used to extract the temporal pattern that represents time delays in the reservoir water level. The patterns are used as input to the ANN model.The results show that a model with 4 days of time delay has produced the acceptable performance with both low error rate and high accuracy. 2015-08-11 Conference or Workshop Item PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/15657/1/PID203.pdf Ashaary, Nur Athirah and Wan Ishak, Wan Hussain and Ku-Mahamud, Ku Ruhana (2015) Forecasting model for the change of reservoir water level stage based on temporal pattern of reservoir water level. In: 5th International Conference on Computing and Informatics (ICOCI) 2015, 11-13 August 2015, Istanbul, Turkey. http://www.icoci.cms.net.my/proceedings/2015/TOC.html
spellingShingle QA75 Electronic computers. Computer science
Ashaary, Nur Athirah
Wan Ishak, Wan Hussain
Ku-Mahamud, Ku Ruhana
Forecasting model for the change of reservoir water level stage based on temporal pattern of reservoir water level
title Forecasting model for the change of reservoir water level stage based on temporal pattern of reservoir water level
title_full Forecasting model for the change of reservoir water level stage based on temporal pattern of reservoir water level
title_fullStr Forecasting model for the change of reservoir water level stage based on temporal pattern of reservoir water level
title_full_unstemmed Forecasting model for the change of reservoir water level stage based on temporal pattern of reservoir water level
title_short Forecasting model for the change of reservoir water level stage based on temporal pattern of reservoir water level
title_sort forecasting model for the change of reservoir water level stage based on temporal pattern of reservoir water level
topic QA75 Electronic computers. Computer science
url https://repo.uum.edu.my/id/eprint/15657/1/PID203.pdf
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AT kumahamudkuruhana forecastingmodelforthechangeofreservoirwaterlevelstagebasedontemporalpatternofreservoirwaterlevel