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:	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General)
Online Access:	http://eprints.intimal.edu.my/2052/ http://eprints.intimal.edu.my/2052/1/jods2024_53.pdf http://eprints.intimal.edu.my/2052/2/593

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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
building	INTI Institutional Repository
collection	Online Access
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.
first_indexed	2025-11-14T11:58:36Z
format	Article
id	intimal-2052
institution	INTI International University
institution_category	Local University
language	English English
last_indexed	2025-11-14T11:58:36Z
publishDate	2024
publisher	INTI International University
recordtype	eprints
repository_type	Digital Repository
spelling	intimal-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
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)
title	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_short	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)
topic	QA75 Electronic computers. Computer science QA76 Computer software T Technology (General)
url	http://eprints.intimal.edu.my/2052/ http://eprints.intimal.edu.my/2052/ http://eprints.intimal.edu.my/2052/1/jods2024_53.pdf http://eprints.intimal.edu.my/2052/2/593

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

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