Application of Machine Learning in Daily Reservoir Inflow Prediction of the Bhumibol Dam, Thailand

STL-decomposition ensemble deep learning models for daily reservoir inflow forecast for hydroelectricity production

“…To solve this problem, machine learning methods have been applied to model them for efficient sustainability [ 5 ]. In an attempt to further increase these models' efficiency, deep learning neural network models have been applied to reservoir inflow forecasting with promising results [ [6] , [7] , [8] , [9] , [10] , [11] , [12] ] Recurrent Neural Networks (RNN), Gated Recurrent Unit (GRU), and Long-Short Tem Memory (LSTM) models have been used for reservoir inflow forecasting, with LSTM proving to be the most effective [ 13 ]. A hybrid framework using machine learning for reservoir inflow forecast has been proposed by Tian et al [ 14 ] with interesting results as an outcome as compared to classical methods.…”