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Bao Wang, Shichao Liu, Bin Wang, Wenzhou Wu, Jiechen Wang, Dingtao Shen. Multi-step ahead short-term predictions of storm surge level using CNN and LSTM network[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1763-9
Citation: Bao Wang, Shichao Liu, Bin Wang, Wenzhou Wu, Jiechen Wang, Dingtao Shen. Multi-step ahead short-term predictions of storm surge level using CNN and LSTM network[J]. Acta Oceanologica Sinica. doi: 10.1007/s13131-021-1763-9

Multi-step ahead short-term predictions of storm surge level using CNN and LSTM network

doi: 10.1007/s13131-021-1763-9
Funds:  The National Key Research and Development Program of China (2016YFC1402609), Open Fund of the Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources (LOMF 1804) and National Natural Science Foundation of China (42077438).
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  • Corresponding author: dingtaoshen@outlook.com
  • Received Date: 2020-10-07
  • Accepted Date: 2020-11-13
  • Available Online: 2021-06-29
  • Storm surges pose significant danger and havoc to the coastal residents’ safety, property, and lives, particularly at offshore locations with shallow water levels. Predictions of storm surges with hours of warning time are important for evacuation measures in low-lying regions and coastal management plans. In addition to experienced predictions and numerical models, artificial intelligence (AI) techniques are also being used widely for short-term storm surge prediction owing to their merits in good level of prediction accuracy and rapid computations. Convolutional neural networks (CNN) and long short-term memory (LSTM) are two of the most important models among AI techniques. However, they have been scarcely utilised for surge level (SL) forecasting, and combinations of the two models are even rarer. This study applied CNN and LSTM both individually and in combination towards multi-step ahead short-term storm surge level prediction using observed SL and wind information. The architectures of the CNN, LSTM, and two sequential techniques of combining the models (LSTM–CNN and CNN–LSTM) were constructed via a trial-and-error approach and knowledge obtained from previous studies. As a case study, 11 a of hourly observed SL and wind data of Xiuying station, Hainan Province, China, were organised as inputs for training to verify the feasibility and superiority of the proposed models. The results show that CNN and LSTM had evident advantages over support vector regression (SVR) and multilayer perceptron (MLP), and the combined models outperformed the individual models (CNN and LSTM), mostly by 4%–6%. However, on comparing the model computed predictions during two severe typhoons that resulted in extreme storm surges, the accuracy was found to improve by over 10% at all forecasting steps.
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