# Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre
## 6 Conclusions  
Throughout the set of experiments that were set up for testing the performance of the model, a very noticeable pattern emerged. The machine learning storm surge forecasting model created in this study was able to constantly provide an accurate estimation of everyday conditions with an average RMSE of 0.0813 among the buoy stations presented. Let us recall that these conditions refer to the absence of any impactful weather event that could change the water surface elevations from the expected astronomical tide harmonics. That is, the model was able to capture the harmonic future oscillations from previous observations while considering normal conditions of winds and pressure. This accomplishment is mainly due to the presence of the LSTM architecture. Ref. [21] demonstrated that LSTM networks are capable of predicting tidal levels effectively with superior performance when compared with other methodologies. Being able to correctly predict tidal levels is imperative to predicting storm surges, since the oscillation of tides have an additive or subtracting effect on the final elevations, as it was noted in Section 5.1. Once this baseline was accomplished during the modeling phase, it was important to build upon it. The next logical step was to attempt to incorporate weather information into the model. The inclusion of the CNN architecture improved everyday conditions and allowed for the estimation of storm surge conditions due to the consideration of input from weather events. On a more specific note, in the preliminary model results presented in Section 5.1.3, a view of the overall performance of the final storm surge model and the importance of the inclusion of both CNN and LSTM architectures can be seen. The result for normal conditions in the Port Isabel recording station is greatly improved in the CNN+LSTM preliminary model from Figure 7 when compared directly with the CNN model prediction from Figure 4, highlighting the importance of the inclusion of the LSTM architecture, as mentioned. There was a reduction in RMSE from 0.3231 for the CNN-only model to 0.1037 for the CNN+LSTM model. The contribution of the CNN architecture to the final model is also important. Figure 6 shows a delayed response of the predicted storm surge in the presence of a storm event and an RMSE of 0.1555. For storm surge conditions, the inclusion of forecasted weather gridded data as input for the CNN head of the model provides a considerable improvement not only in peak prediction but in its timing, as it is seen in Figure 8. This change further reduced the RMSE to 0.0860 during the same period of predicted time. In Section 5.2, the prediction of normal conditions and storm surge conditions for the final model shows an important point. Overall, normal condition predictions have a maximum RMSE of 0.1058 and an average RMSE of 0.0813. In the case of storm surge conditions, the maximum RMSE is 0.1632 and the average is 0.0996. From this, we can note that the predictions for storm surge conditions are worse than for normal conditions in the final model. This is important, as it shows a limitation in the storm surge forecasting model presented. However, this limitation in accuracy can be directly attributed to the lack of storm surge data training samples in comparison with normal conditions. The objective of the model presented was to be a storm surge forecasting tool, as such, forecasting-type data were used to train it. These data were obtained from NAM [29] forecasts on windsand atmospheric pressure. This limited the amount of data available, and while there are big collections of parametric data, such as HURDAT2 [30], they were not used in the study because the data are generated after an event and not as a forecast, which could not be directly leveraged in this storm surge forecasting model. Another constraint in data comes from the focus on the Lower Laguna Madre area. A greater model domain will provide better data and plentiful storm events to improve the performance of the model.