# Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre
## 5 Results
### Machine Learning Storm Surge Forecasting Model Results
#### 5.2.2 Storm Surge Conditions  
Hurricane Dolly predictions can be seen in Figure 11. The RMSEs for the different stations during Hurricane Dolly are 0.1312, 0.1632, 0.0462, and 0.1430 for nodes 60551, 42195, 59162, and 61388, respectively. There is more spread in these predictions; however, the peak surges observed and predicted do not differ greatly. On average, there is a 0.1183 m difference between the peak observed and the one predicted.  
In the case of Hurricane Alex in 2010, the RMSEs for the four stations that can be seen in Figure 12 are 0.065, 0.0822, 0.0549, and 0.075, respectively. The model performed much better during this event than for Hurricane Dolly. The average difference between the observed and predicted peaks is much smaller compared with that of Dolly, being 0.0422 m.  
Finally, for Hurricane Hanna in 2020, the RMSE for each of the buoy stations in Figure 13 is 0.1142, 0.1105, 0.0835, and 0.1268, respectively. Interestingly, the peak observed and predicted average difference is \(-0.0337\) m, meaning, the model tended to overestimate storm surge peaks.  
Figure 10: Scatter plot for four nodes in the CNN+LSTM final machine learning model for the month of June 2020.  
Figure 9: Scatter plot for four nodes in the CNN+LSTM final machine learning model for the month of June 2008.  
For most cases, the model was able to produce better estimations for everyday conditions in comparison with storm surge conditions. Limitations in data availability contributed to these results. Data representative of normal conditions constitute nearly 94% of the samples, with the rest corresponding to storm surge conditions. The limitation in data is attributed to the area that the study was conducted in, where, since 2008, around 20 abnormal ocean elevations due to weather were recorded. To improve results without modifying the model, the amount of samples representative of storm surge conditions must be increased. To accomplish this, in the field of image recognition, there are a couple of techniques that allow for the creation of more samples for training. There is no use for these algorithms for the perturbation of existing gridded weather forecasts, since the information  
Figure 11: Scatter plot for four nodes in the CNN+LSTM final machine learning model for the month of July 2008.  
Figure 12: Scatter plot for four nodes in the CNN+LSTM final machine learning model for the month of June 2010.  
Figure 13: Scatter plot for four nodes in the CNN+LSTM final machine learning model for the month of July 2020.  
contained in the samples is the result of complex atmospheric phenomena, and without a numerical model it is not feasible to perturb the information contained in the samples with traditional image data augmentation algorithms. Nonetheless, the generation of new samples for the improvement of the described machine learning storm surge model is out of the scope of this study. Other possible improvements to the model could come from the input considered which, in essence, is the same type of input used in the numerical model but transformed into a different format. Perhaps the application of feature engineering techniques to the features in the input data could yield better results and a different model architecture that considers a new type of input. Another possible route could be creating an ensemble of models whose output is averaged, which could be achievable without changing the underlying architecture of the model or the input.