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Comparison of Machine Learning Models in Forecasting Reservoir Water Level

Reservoirs are important for flood mitigation and water supply storage. The reservoir water release decision, however, must be intelligently modeled due to the unknown volume of input. The model can help reservoir operators make early water release decisions during heavy rainstorms and hold water du...

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Main Authors: Aquil, M.A.I., Ishak, W.H.W.
Format: Article
Published: 2023
Online Access:http://scholars.utp.edu.my/id/eprint/37614/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168459732&doi=10.37934%2faraset.31.3.137144&partnerID=40&md5=dd5fcc9dd4785a47a2eb4fafc665f09b
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spelling oai:scholars.utp.edu.my:376142023-10-13T13:05:05Z http://scholars.utp.edu.my/id/eprint/37614/ Comparison of Machine Learning Models in Forecasting Reservoir Water Level Aquil, M.A.I. Ishak, W.H.W. Reservoirs are important for flood mitigation and water supply storage. The reservoir water release decision, however, must be intelligently modeled due to the unknown volume of input. The model can help reservoir operators make early water release decisions during heavy rainstorms and hold water during drought seasons. One of the promising techniques has been a machine learning-based forecasting model. Therefore, in this study, several machine learning models were identified and compared in terms of performance using Mean Absolute Error (MAE), R-Square, and Root Mean Square (RMSE). The findings show that VARMAX has the highest R-squared value. This identifies the data set as a time series having a seasonal component. ARIMA, on the other hand, is unable to produce adequate results when a seasonal component is included. Both models' MAE and RMSE values accurately reflect the above-mentioned argument. © 2023, Penerbit Akademia Baru. All rights reserved. 2023 Article NonPeerReviewed Aquil, M.A.I. and Ishak, W.H.W. (2023) Comparison of Machine Learning Models in Forecasting Reservoir Water Level. Journal of Advanced Research in Applied Sciences and Engineering Technology, 31 (3). pp. 137-144. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168459732&doi=10.37934%2faraset.31.3.137144&partnerID=40&md5=dd5fcc9dd4785a47a2eb4fafc665f09b 10.37934/araset.31.3.137144 10.37934/araset.31.3.137144 10.37934/araset.31.3.137144
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Reservoirs are important for flood mitigation and water supply storage. The reservoir water release decision, however, must be intelligently modeled due to the unknown volume of input. The model can help reservoir operators make early water release decisions during heavy rainstorms and hold water during drought seasons. One of the promising techniques has been a machine learning-based forecasting model. Therefore, in this study, several machine learning models were identified and compared in terms of performance using Mean Absolute Error (MAE), R-Square, and Root Mean Square (RMSE). The findings show that VARMAX has the highest R-squared value. This identifies the data set as a time series having a seasonal component. ARIMA, on the other hand, is unable to produce adequate results when a seasonal component is included. Both models' MAE and RMSE values accurately reflect the above-mentioned argument. © 2023, Penerbit Akademia Baru. All rights reserved.
format Article
author Aquil, M.A.I.
Ishak, W.H.W.
spellingShingle Aquil, M.A.I.
Ishak, W.H.W.
Comparison of Machine Learning Models in Forecasting Reservoir Water Level
author_facet Aquil, M.A.I.
Ishak, W.H.W.
author_sort Aquil, M.A.I.
title Comparison of Machine Learning Models in Forecasting Reservoir Water Level
title_short Comparison of Machine Learning Models in Forecasting Reservoir Water Level
title_full Comparison of Machine Learning Models in Forecasting Reservoir Water Level
title_fullStr Comparison of Machine Learning Models in Forecasting Reservoir Water Level
title_full_unstemmed Comparison of Machine Learning Models in Forecasting Reservoir Water Level
title_sort comparison of machine learning models in forecasting reservoir water level
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/37614/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85168459732&doi=10.37934%2faraset.31.3.137144&partnerID=40&md5=dd5fcc9dd4785a47a2eb4fafc665f09b
_version_ 1781707932540338176
score 13.214268

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