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Machine learning application in reservoir water level forecasting for sustainable hydropower generation strategy

alternative energy; data set; hydroelectric power plant; machine learning; power generation; precipitation intensity; scenario analysis; sustainable development; uncertainty analysis; water level; Malaysia

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Main Authors: Sapitang M., Ridwan W.M., Kushiar K.F., Ahmed A.N., El-Shafie A.
Other Authors: 57215211508
Format: Article
Published: MDPI 2023
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spelling my.uniten.dspace-253692023-05-29T16:08:37Z Machine learning application in reservoir water level forecasting for sustainable hydropower generation strategy Sapitang M. Ridwan W.M. Kushiar K.F. Ahmed A.N. El-Shafie A. 57215211508 57218502036 57212462702 57214837520 16068189400 alternative energy; data set; hydroelectric power plant; machine learning; power generation; precipitation intensity; scenario analysis; sustainable development; uncertainty analysis; water level; Malaysia The aim of this study is to accurately forecast the changes in water level of a reservoir located in Malaysia with two different scenarios; Scenario 1 (SC1) includes rainfall and water level as input and Scenario 2 (SC2) includes rainfall, water level, and sent out. Different time horizons (one day ahead to seven days) will be investigated to check the accuracy of the proposed models. In this study, four supervised machine learning algorithms for both scenarios were proposed such as Boosted Decision Tree Regression (BDTR), Decision Forest Regression (DFR), Bayesian Linear Regression (BLR) and Neural Network Regression (NNR). Eighty percent of the total data were used for training the datasets while 20% for the dataset used for testing. The models' performance is evaluated using five statistical indexes; the Correlation Coefficient (R2), Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Relative Absolute Error (RAE), and Relative Squared Error (RSE). The findings showed that among the four proposed models, the BLR model outperformed other models with R2 0.998952 (1-day ahead) for SC1 and BDTR for SC2 with R2 0.99992 (1-day ahead). With regards to the uncertainty analysis, 95PPU and d-factors were adopted to measure the uncertainties of the best models (BLR and BDTR). The results showed the value of 95PPU for both models in both scenarios (SC1 and SC2) fall into the range between 80% to 100%. As for the d-factor, all values in SC1 and SC2 fall below one. � 2020 by the authors. Final 2023-05-29T08:08:37Z 2023-05-29T08:08:37Z 2020 Article 10.3390/su12156121 2-s2.0-85089340528 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85089340528&doi=10.3390%2fsu12156121&partnerID=40&md5=8382fbe8ea2f363992c452cf190c323c https://irepository.uniten.edu.my/handle/123456789/25369 12 15 6121 All Open Access, Gold, Green MDPI 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 alternative energy; data set; hydroelectric power plant; machine learning; power generation; precipitation intensity; scenario analysis; sustainable development; uncertainty analysis; water level; Malaysia
author2 57215211508
author_facet 57215211508
Sapitang M.
Ridwan W.M.
Kushiar K.F.
Ahmed A.N.
El-Shafie A.
format Article
author Sapitang M.
Ridwan W.M.
Kushiar K.F.
Ahmed A.N.
El-Shafie A.
spellingShingle Sapitang M.
Ridwan W.M.
Kushiar K.F.
Ahmed A.N.
El-Shafie A.
Machine learning application in reservoir water level forecasting for sustainable hydropower generation strategy
author_sort Sapitang M.
title Machine learning application in reservoir water level forecasting for sustainable hydropower generation strategy
title_short Machine learning application in reservoir water level forecasting for sustainable hydropower generation strategy
title_full Machine learning application in reservoir water level forecasting for sustainable hydropower generation strategy
title_fullStr Machine learning application in reservoir water level forecasting for sustainable hydropower generation strategy
title_full_unstemmed Machine learning application in reservoir water level forecasting for sustainable hydropower generation strategy
title_sort machine learning application in reservoir water level forecasting for sustainable hydropower generation strategy
publisher MDPI
publishDate 2023
_version_ 1806427727280996352
score 13.214268

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