# Storm Surge Prediction Based on Long Short-Term Memory Neural Network in the East China Sea
## 3 Experimental Process and Result Analysis
### Model Results of LSTM  
The 12 processes of storm surge with the meteorological data (air pressure, wind speed, wind direction) and the water level data which were recorded at Lusi tidal station in Jiangsu Province, China from 1986 to 2016 are used to train the hyperparameters of the LSTM model. Although some scholars used neural network to predict the time-varying storm surge which is calculated by subtracting the astronomical tidal level from the total water level [57], the data used in this study is the total water level including the pure astronomical tidal level and the typhoon-induced water level due to that the key criterion usually considered by the marine management departments is the total water level, especially the peak water level prone to exceed the warning limit.  
Figure 4 shows the model results of the LSTM model with 1 h prediction time and its ACC value of training set is above 0.95 to guarantee the model precision. The testing set is calculated for prediction of the water level using the hyperparameters derived by training the LSTM model based on the training set. In Figure 4b, the light blue area represents a cold start period of 24 h for testing set calculation and it is not considered into analysis. In terms of the model evaluation for testing set, the values of MAE, RMSE, and ACC are 13.40, 16.28, and 0.95 respectively, as shown in Table 2. The testing set agrees well with the observed water levels. Since the peak value of water level is the most concern during the time series, the differences of each peak value are calculated and the maximum difference is 18 cm circled by the red dash line in Figure 4b. In general, the 1 h prediction ability of the LSTM model performs quite well.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Prediction** & **LSTM** & **BRR** & **GBDT** & **LR** & **SVR** \\ \hline \multirow{4}{*}{MAE (cm)} & 1 h & 13.40 & 48.80 & 59.52 & 48.80 & 64.08 \\  & 3 h & 33.59 & 96.22 & 102.58 & 96.62 & 105.51 \\  & 7 h & 31.44 & 83.98 & 96.37 & 84.01 & 89.95 \\  & 15 h & 40.70 & 99.25 & 102.50 & 99.58 & 101.61 \\ \hline \multirow{4}{*}{RMSE (cm)} & 1 h & 16.28 & 56.13 & 71.14 & 56.12 & 75.45 \\  & 3 h & 41.06 & 109.30 & 125.06 & 109.89 & 131.49 \\  & 7 h & 42.26 & 98.50 & 116.13 & 98.70 & 110.86 \\  & 15 h & 66.77 & 112.91 & 127.15 & 113.84 & 130.96 \\ \hline \multirow{4}{*}{ACC} & 1 h & 0.95 & 0.83 & 0.81 & 0.83 & 0.80 \\  & 3 h & 0.85 & 0.67 & 0.70 & 0.67 & 0.66 \\  & 7 h & 0.88 & 0.71 & 0.63 & 0.71 & 0.68 \\  & 15 h & 0.86 & 0.66 & 0.65 & 0.66 & 0.61 \\ \hline \multirow{4}{*}{Maximum Difference* (cm)} & 1 h & 18 & 38 & 173 & 150 & 205 \\  & 3 h & 23 & 51 & 129 & 186 & 166 \\ \cline{1-1}  & 7 h & 36 & 38 & 172 & 149 & 205 \\ \cline{1-1}  & 15 h & 45 & 114 & 188 & 210 & 278 \\ \hline \hline \end{tabular}  
*: “Maximum Difference” represents the maximum difference between the modelled and observed values of the testing set at the water level peak.  
\end{table}
Table 2: Model evaluation of testing set for different models with four prediction times.