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Forecasting of Reservoir Inflow Using Machine Learning�Case Study: Klang Gate Dam Reservoir

Climate change is a long-term change in the ordinary weather conditions that affects the local, regional and global climates. One of the response solutions to overcome this phenomenon is by managing water resources more efficiently. The reservoir is a major infrastructure to achieve water resource m...

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Main Authors: Hassan K.S.M., Huang Y.F., Koo C.H., Weng T.K., Ahmed A.N., Elshafie A.H.K.A.
Other Authors: 57386567700
Format: Conference Paper
Published: Springer Science and Business Media Deutschland GmbH 2023
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spelling my.uniten.dspace-272842023-05-29T17:42:07Z Forecasting of Reservoir Inflow Using Machine Learning�Case Study: Klang Gate Dam Reservoir Hassan K.S.M. Huang Y.F. Koo C.H. Weng T.K. Ahmed A.N. Elshafie A.H.K.A. 57386567700 55807263900 57204843657 57387317300 57214837520 16068189400 Climate change is a long-term change in the ordinary weather conditions that affects the local, regional and global climates. One of the response solutions to overcome this phenomenon is by managing water resources more efficiently. The reservoir is a major infrastructure to achieve water resource management and therefore it requires accurate water resources forecasting. The SVR and MLPNN models are introduced as a solution to achieve an efficient reservoir inflow forecasting. There are many input parameters that influence reservoir water flow but the 3 most important parameters are storage level, rainfall, and evaporation that need to be fed into the two models. There have been various model parameters tested such as kernel types in the SVR and the number of hidden layers and neurons in the MLPNN. Both models have proven their ability but however, the MLPNN with two hidden layers and 4 neurons in each layer had outperformed the SVR after being tested using four different performance tests. � 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. Final 2023-05-29T09:42:07Z 2023-05-29T09:42:07Z 2022 Conference Paper 10.1007/978-3-030-85990-9_4 2-s2.0-85121815458 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85121815458&doi=10.1007%2f978-3-030-85990-9_4&partnerID=40&md5=2c5df2f8728d911599f3a335588c19f8 https://irepository.uniten.edu.my/handle/123456789/27284 322 33 47 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Climate change is a long-term change in the ordinary weather conditions that affects the local, regional and global climates. One of the response solutions to overcome this phenomenon is by managing water resources more efficiently. The reservoir is a major infrastructure to achieve water resource management and therefore it requires accurate water resources forecasting. The SVR and MLPNN models are introduced as a solution to achieve an efficient reservoir inflow forecasting. There are many input parameters that influence reservoir water flow but the 3 most important parameters are storage level, rainfall, and evaporation that need to be fed into the two models. There have been various model parameters tested such as kernel types in the SVR and the number of hidden layers and neurons in the MLPNN. Both models have proven their ability but however, the MLPNN with two hidden layers and 4 neurons in each layer had outperformed the SVR after being tested using four different performance tests. � 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
author2 57386567700
author_facet 57386567700
Hassan K.S.M.
Huang Y.F.
Koo C.H.
Weng T.K.
Ahmed A.N.
Elshafie A.H.K.A.
format Conference Paper
author Hassan K.S.M.
Huang Y.F.
Koo C.H.
Weng T.K.
Ahmed A.N.
Elshafie A.H.K.A.
spellingShingle Hassan K.S.M.
Huang Y.F.
Koo C.H.
Weng T.K.
Ahmed A.N.
Elshafie A.H.K.A.
Forecasting of Reservoir Inflow Using Machine Learning�Case Study: Klang Gate Dam Reservoir
author_sort Hassan K.S.M.
title Forecasting of Reservoir Inflow Using Machine Learning�Case Study: Klang Gate Dam Reservoir
title_short Forecasting of Reservoir Inflow Using Machine Learning�Case Study: Klang Gate Dam Reservoir
title_full Forecasting of Reservoir Inflow Using Machine Learning�Case Study: Klang Gate Dam Reservoir
title_fullStr Forecasting of Reservoir Inflow Using Machine Learning�Case Study: Klang Gate Dam Reservoir
title_full_unstemmed Forecasting of Reservoir Inflow Using Machine Learning�Case Study: Klang Gate Dam Reservoir
title_sort forecasting of reservoir inflow using machine learning�case study: klang gate dam reservoir
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806426253712949248
score 13.211869

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