期刊
JOURNAL OF HYDROLOGY
卷 583, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.jhydrol.2019.124296
关键词
Long short-term memory; Wavelet transform; Convolutional layers; Hydrometeorological variables prediction
资金
- National Key Research and Development Program of China, China [2017YFC1502704, 2016YFC0401501]
- National Natural Science Foundation of China, China [41571017, 51679118, 91647203]
- Jiangsu Province333 Project, China [BRA2018060]
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.
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