Water Level Prediction of Riam Kanan Dam Using ConvLSTM, BPNN, Gradient Boosting, and XGBoosting Stacking Framework (CLBGXGBoostS)

Research focuses on developing a water level prediction framework for the Riam Kanan Dam using an innovative stacking approach called ConvLSTM-BPNN-Gradient Boosting and Stacking XGBoost (CLBGXGBoostS), which combines the strengths of Convolutional Long Short-Term Memory (ConvLSTM), Backpropagati...

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Main Authors: Usman, Syapotro, Haldi, Budiman, M.Rezqy, Noor Ridha, Noor, Azijah
Format: Article
Language:English
English
Published: INTI International University 2024
Subjects:
Online Access:http://eprints.intimal.edu.my/2052/1/jods2024_53.pdf
http://eprints.intimal.edu.my/2052/2/593
http://eprints.intimal.edu.my/2052/
http://ipublishing.intimal.edu.my/jods.html
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spelling my-inti-eprints.20522024-11-26T06:44:05Z http://eprints.intimal.edu.my/2052/ Water Level Prediction of Riam Kanan Dam Using ConvLSTM, BPNN, Gradient Boosting, and XGBoosting Stacking Framework (CLBGXGBoostS) Usman, Syapotro Haldi, Budiman M.Rezqy, Noor Ridha Noor, Azijah QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) Research focuses on developing a water level prediction framework for the Riam Kanan Dam using an innovative stacking approach called ConvLSTM-BPNN-Gradient Boosting and Stacking XGBoost (CLBGXGBoostS), which combines the strengths of Convolutional Long Short-Term Memory (ConvLSTM), Backpropagation Neural Network (BPNN), and Gradient Boosting. The study aims to evaluate the performance of the CLBGXGBoostS stacking framework in predicting the water level of the Riam Kanan Dam using 5 years of historical data. The results demonstrate that the CLBGXGBoostS framework provides more accurate predictions compared to single models, as evidenced by the Root Mean Squared Error (RMSE) values. CLBGXGBoostS achieves an RMSE of 0.0071, significantly lower than the RMSE of the individual models ConvLSTM (0.1006), BPNN (0.2618), and Gradient Boosting (0.6905). This research contributes to the development of a better water level prediction framework for the Riam Kanan Dam, supporting more effective water resource management and serving as a reference for future research in this field. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2052/1/jods2024_53.pdf text en cc_by_4 http://eprints.intimal.edu.my/2052/2/593 Usman, Syapotro and Haldi, Budiman and M.Rezqy, Noor Ridha and Noor, Azijah (2024) Water Level Prediction of Riam Kanan Dam Using ConvLSTM, BPNN, Gradient Boosting, and XGBoosting Stacking Framework (CLBGXGBoostS). Journal of Data Science, 2024 (53). pp. 1-5. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html
institution INTI International University
building INTI Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider INTI International University
content_source INTI Institutional Repository
url_provider http://eprints.intimal.edu.my
language English
English
topic QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
spellingShingle QA75 Electronic computers. Computer science
QA76 Computer software
T Technology (General)
Usman, Syapotro
Haldi, Budiman
M.Rezqy, Noor Ridha
Noor, Azijah
Water Level Prediction of Riam Kanan Dam Using ConvLSTM, BPNN, Gradient Boosting, and XGBoosting Stacking Framework (CLBGXGBoostS)
description Research focuses on developing a water level prediction framework for the Riam Kanan Dam using an innovative stacking approach called ConvLSTM-BPNN-Gradient Boosting and Stacking XGBoost (CLBGXGBoostS), which combines the strengths of Convolutional Long Short-Term Memory (ConvLSTM), Backpropagation Neural Network (BPNN), and Gradient Boosting. The study aims to evaluate the performance of the CLBGXGBoostS stacking framework in predicting the water level of the Riam Kanan Dam using 5 years of historical data. The results demonstrate that the CLBGXGBoostS framework provides more accurate predictions compared to single models, as evidenced by the Root Mean Squared Error (RMSE) values. CLBGXGBoostS achieves an RMSE of 0.0071, significantly lower than the RMSE of the individual models ConvLSTM (0.1006), BPNN (0.2618), and Gradient Boosting (0.6905). This research contributes to the development of a better water level prediction framework for the Riam Kanan Dam, supporting more effective water resource management and serving as a reference for future research in this field.
format Article
author Usman, Syapotro
Haldi, Budiman
M.Rezqy, Noor Ridha
Noor, Azijah
author_facet Usman, Syapotro
Haldi, Budiman
M.Rezqy, Noor Ridha
Noor, Azijah
author_sort Usman, Syapotro
title Water Level Prediction of Riam Kanan Dam Using ConvLSTM, BPNN, Gradient Boosting, and XGBoosting Stacking Framework (CLBGXGBoostS)
title_short Water Level Prediction of Riam Kanan Dam Using ConvLSTM, BPNN, Gradient Boosting, and XGBoosting Stacking Framework (CLBGXGBoostS)
title_full Water Level Prediction of Riam Kanan Dam Using ConvLSTM, BPNN, Gradient Boosting, and XGBoosting Stacking Framework (CLBGXGBoostS)
title_fullStr Water Level Prediction of Riam Kanan Dam Using ConvLSTM, BPNN, Gradient Boosting, and XGBoosting Stacking Framework (CLBGXGBoostS)
title_full_unstemmed Water Level Prediction of Riam Kanan Dam Using ConvLSTM, BPNN, Gradient Boosting, and XGBoosting Stacking Framework (CLBGXGBoostS)
title_sort water level prediction of riam kanan dam using convlstm, bpnn, gradient boosting, and xgboosting stacking framework (clbgxgboosts)
publisher INTI International University
publishDate 2024
url http://eprints.intimal.edu.my/2052/1/jods2024_53.pdf
http://eprints.intimal.edu.my/2052/2/593
http://eprints.intimal.edu.my/2052/
http://ipublishing.intimal.edu.my/jods.html
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score 13.222552