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

Bao Wang; Shichao Liu; Bin Wang; Wenzhou Wu; Jiechen Wang; Dingtao Shen

doi:10.1007/s13131-021-1763-9

Volume 40 Issue 11

Nov. 2021

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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, 2021, 40(11): 104-118. doi: 10.1007/s13131-021-1763-9

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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, 2021, 40(11): 104-118. doi: 10.1007/s13131-021-1763-9

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

doi: 10.1007/s13131-021-1763-9

Bao Wang^{1, 2},
Shichao Liu²,
Bin Wang²,
Wenzhou Wu³,
Jiechen Wang^{1, 4},
Dingtao Shen^{5
,
,}

1.
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology/Key Laboratory for Land Satellite Remote Sensing Applications of Ministry of Natural Resources/School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
2.
Key Laboratory of Marine Hazards Forecasting, National Marine Environmental Forecasting Center, Ministry of Natural Resources, Beijing 100081, China
3.
State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
4.
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing University, Nanjing 210023, China
5.
Changjiang River Scientific Research Institute, Changjiang Water Resources Commission, Wuhan 430010, China

Funds: The National Key Research and Development Program of China under contract No. 2016YFC1402609; the Open Fund of the Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources under contract No. LOMF 1804; the National Natural Science Foundation of China under contract No. 42077438.

More Information

Corresponding author: dingtaoshen@outlook.com
Received Date: 2020-10-07
Accepted Date: 2020-11-13

Available Online: 2021-06-29

Publish Date: 2021-11-30

Abstract

Abstract

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 network (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 the 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.
- storm surge,
- prediction,
- CNN,
- LSTM,
- combination

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References(41)

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