Research on typhoon wave height prediction method based on BO-LSTM neural network model

Qin Zhipeng; Chen Yongping; Pan Yi; Xu Xiaowu

doi:10.12284/hyxb2024089

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Qin Zhipeng，Chen Yongping，Pan Yi, et al. Research on typhoon wave height prediction method based on BO-LSTM neural network model[J]. Haiyang Xuebao，2024, 46(10)：1–10 doi: 10.12284/hyxb2024089

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Research on typhoon wave height prediction method based on BO-LSTM neural network model

doi: 10.12284/hyxb2024089

Qin Zhipeng^{1, 2
,},
Chen Yongping^{1, 2
,
,},
Pan Yi²,
Xu Xiaowu³

1.
The National Key Laboratory of Water Disaster Prevention , Hohai University, Nanjing 210098, China
2.
College of Harbour, Coastal and Offshore Engineering, Hohai University, Nanjing 210098, China
3.
Marine Monitoring and Forecasting Center of Zhejiang Province, Hangzhou 310007, China

Received Date: 2024-01-03
Rev Recd Date: 2024-07-15

Available Online: 2024-10-18

Abstract

Abstract

With the impact of climate change such as rising sea levels and intensified storms, it is particularly important to quickly and accurately predict typhoon wave heights for coastal protection and marine disaster prevention. This article first generates a large number of virtual typhoons based on the TCWiSE model, uses the SWAN numerical model to calculate the significant wave height at the observation station during the typhoon, and constructs a sample database of typhoon waves; Then evaluate and select the input factors and hyperparameters of the BO-LSTM neural network model, and train and test it using a sample database. The results show that the constructed virtual typhoon has good similarity with historical typhoons, which can provide sufficient data basis for the construction of intelligent typhoon wave height prediction models; The BO-LSTM model built can quickly achieve intelligent prediction of typhoon wave height at a single station, and has prediction accuracy comparable to SWAN. Its prediction accuracy in long-term forecasting scenarios is significantly better than RF and BPNN models; Adding future typhoon data to the input of the BO-LSTM model further improves the accuracy and duration of the model’s forecast. Its Bias, RMSE, and R² for predicting the next 24 h are −0.102 m, 0.494 m, and 0.855, respectively. The research results provide a feasible approach for intelligent forecasting of typhoon waves under extreme weather conditions.
- typhoon waves,
- wave height forecast,
- LSTM model,
- bayesian optimization,
- virtual typhoon

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References(24)

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