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

• 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/2052/1/jods2024_53.pdf; http://eprints.intimal.edu.my/2052/2/593; http://eprints.intimal.edu.my/2052/; http://ipublishing.intimal.edu.my/jods.html

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...