Intelligent wave forecasting and evaluation along the southeast coast of China based on ConvLSTM method

doi:10.3969/j.issn.1001-909X.2024.03.007

Abstract

Abstract:

Compared with the semi-theoretical and semi-analytical wave forecasting and numerical modeling,artificial intelligence wave forecasting has the advantages of higher forecasting accuracy and lower computational resource requirements. In this paper, a two-dimensional significant wave height (SWH) forecasting model for the southeast coast of China is established based on the convolutional long short-term memory network (ConvLSTM) algorithm using ERA5 (ECMWF Reanalysis v5) reanalysis data as the initial field. The data from 2014-2022 are used to train the forecasts of SWH for the next 6 h, 12 h, 18 h and 24 h, and the data from 2023 are used for testing. Sensitivity tests are carried out to optimize the model configuration and evaluate the prediction performance of SWH in the southeast coast of China at four period validity (6 h, 12 h, 18 h, 24 h) in 2023. Sensitivity tests show that when input time series length N=4(input SWH value of -18 h, -12 h, -6 h, 0 h), the accuracies of the model at four period validity are better than those of other time series length. When the combination of input physical elements is SWH, mean wave direction and sea surface 10 m wind vector, the accuracy of the model is better than other combinations at 12 h, 18 h and 24 h. Through the fine-tuning of ConvLSTM model training and configuration, the two-dimensional and high-precision intelligent prediction of SWH in the southeast coast of China can be realized.

Key words: coast of China, convolutional long short-term memory network (ConvLSTM), data-driven, wave, significant wave height (SWH), two-dimensional prediction model, short-time forecast, artificial intelligence, deep learning

CLC Number:

P731.33
P714

JIN Yang, HAN Lei, JIN Meibing, DONG Changming. Intelligent wave forecasting and evaluation along the southeast coast of China based on ConvLSTM method[J]. Journal of Marine Sciences, 2024, 42(3): 88-98.

Figures/Tables 10

Fig.1 The topography and water depth in the southeast seas of China

Fig.2 Schematic diagram of the structure of the SWH prediction model

Fig.3 Evaluation indexes of ConvLSTM model prediction performance with different time series length

Fig.4 Seasonal difference of ConvLSTM model prediction performance evaluation indexes

Fig.5 Observed SWH and predicted SWH of 2023 spring: ERA5 (a-d), ConvLSTM model (N=4, e-h) and the relative variability between model and ERA5 (i-l)

Fig.6 Spatial distribution of ConvLSTM model prediction results evaluation indexes

Tab.1 ConvLSTM model configuration for the sensitivity test of physical elements

试验编号	输入的物理要素
2.1	SWH
2.2	SWH、平均波向
2.3	海面10 m经向风速、海面10 m纬向风速
2.4	SWH、海面10 m 经向风速、海面10 m纬向风速
2.5	SWH、平均波向、海面10 m纬向风速
2.6	SWH、平均波向、海面10 m经向风速、海面10 m纬向风速

Fig.7 Evaluation of ConvLSTM model prediction performance in different experiment groups

Fig.8 Observed SWH and predicted SWH of 2023 spring: ERA5 (a-d), ConvLSTM model based on experiment 2.4 configuration (e-h) and the relative variability between model and ERA5 (i-l)

Fig.9 Performance evaluation of ConvLSTM model based on experiment 2.4 configuration

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