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: | , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| 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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| Summary: | 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. |
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