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Streamflow and rainfall forecasting by two long short-term memory-based models
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DOI：10.1016/j.jhydrol.2019.124296.png)
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Abstract
En 中文
Prediction of streamflow and rainfall is important for water resources planning and management. In this study, we developed two hybrid models, based on long short-term memory network (LSTM), for monthly streamflow and rainfall forecasting. One model, wavelet-LSTM (namely, WLSTM), applied a trous algorithm of wavelet transform to do series decomposition, and the other, convolutional LSTM (namely, CLSTM), coupled convolutional neural network to extract temporal features. Two streamflow datasets and two rainfall datasets are used to evaluate the proposed models. The prediction accuracy of WLSTM and CLSTM was compared with that of multilayer perceptron (MLP) and LSTM. Results indicated that LSTM was applicable for time series prediction, but WLSTM and CLSTM were superior alternatives.
Keywords:
Long short-term memory
Wavelet transform
Convolutional layers
Hydrometeorological variables prediction
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