Improving flood forecasting using time-distributed CNN-LSTM model: a time-distributed spatiotemporal method

The rapid and devastating nature of flood events in small and medium basins presents considerable challenges to flood forecasting. Developing a robust and accurate flood forecasting method is crucial to mitigating flood effects. To this end, we propose the Time-distributed CNN-LSTM model (TD-CNN-LSTM), a hybrid deep learning framework based on a Time Distribution layer (TD), Convolutional Neural Networks (CNNs), and Long-Short Term Memory Networks (LSTMs). The TD-CNN-LSTM model efficiently captures spatiotemporal hydrological features within spatial dimensions using the Time-distributed local Features extractor, while the LSTM focuses on extracting spatiotemporal features in temporal dimensions. Our model effectively captures complex spatial and temporal relationship patterns within hydrological data from flood time series. Experimental results on the Tunxi and Changhua basins show the superior predictive capabilities of our model, particularly in forecasting flood occurrence time and peak. When compared with baseline models LSTM, CNN, ConvLSTM, STA-LSTM, and CNN-LSTM at the moment T + 9, TD-CNN-LSTM achieved a 6.7%, 10.14%, 8.5%, 6.3%, and 6.6% decreased in Root-Mean-Square Error (RMSE), respectively, a 6.5%, 9.8%, 10.9%, 6.8%, and 10.6% decreased in Mean Absolute Error (MAE), respectively, and a 7.4%, 31.6%, 23.5%, 18.8%, and 27.8% decreased in Mean Absolute Percentage Error (MAPE), respectively. In addition, the determination Coefficient (R²) increased by 3.6% and Nash-Sutcliffe Efficiency (NSE) increased by 4.8% .