Hybrid deep learning modeling for water level prediction in yangtze river

Abstract

Accurate prediction of water level in inland waterway has been an important issue for helping flood control and vessel navigation in a proactive manner. In this research, a deep learning approach called long short-term memory network combined with discrete wavelet transform (WA-LSTM) is proposed for daily water level prediction. The wavelet transform is applied to decompose time series into details and approximation components for a better understanding of temporal properties, and a novel LSTM network is used to learn generic water level features through layer-by-layer feature granulation with a greedy layer wise unsu-pervised learning algorithm. Six representative reaches in Yangtze River, namely, the Jianli, Wuhan, Jiujiang, Anqing, Wuhu, and Nanjing are investigated, and water level data from 2010 to 2019 are processed through temporal and spatial correlation analysis, and combination-optimized to develop and evaluate the proposed model. In general, the test average performances on RMSE and MAE are less than 0.045 m and 0.035 m respectively, which outperforms the state-of-the-art models, such as WA-ANN, WA-ARIMA and LSTM models. The results indicate that the WA-LSTM model is stable, reliable and widely applicable.