# Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre
## 2 Materials and Methods
### Literature Review  
Machine learning techniques have been employed extensively in the prediction of weather and for the modeling of complex relationships, such as storm surges, precipitation, and floods. Machine learning has proven itself to be a valuable tool in the creation of very accurate, non-resource-intensive models that can capture very complex phenomena. For example, Artificial Neural Networks (ANN) have been utilized for capturing the rainfall-runoff relationship in basins where the declaration of the internal structure of the watershed is not needed [12]. Neural networks have also been used to predict floods with fairly good accuracy [13]. These initially reviewed papers reiterate the possibility of creating a machine learning model for storm surges. Hurricane storm surges are an example of a complex nonlinear relationship where the usage of machine learning methods can prove to be very beneficial. Neural networks are a type of machine learning technique that have already been proven successful for storm surge prediction. In the past, several studies have explored the performance of neural network architectures when it comes to storm surge estimation. Simple ANNs [12; 14; 15; 16; 17; 18; 19; 20; 21] have already succeeded in recognizing the relationship between weather variables and the subsequent storm surge; however, some problems still remain. For a better visualization, Table 1 contains a sample of 10 reviewed studies. As it can be seen from Table 1, most of the studies utilized ANN to produce their storm surge predictions. ANNs accept a fixed amount of predictor variables; the most common predictor variables utilized in the studies reviewed are storm parameters. Some of the parameters are the location of the storm, angle of approach, translation speed of the storm, wind speeds, and radius of strong winds. The accuracies obtained by the studies are good; however, there are limitations that could be improved upon. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Paper** & **Predicted** & **Model** & **Predictors** & **Data Type** & **Metrics** \\ \hline \multirow{8}{*}{[16]} & \multirow{8}{*}{Storm surge} & & Longitude, latitude at & & \\  & & & landfall, heading & & \\  & & & direction, central & & \\ \cline{1-1}  & & & pressure, moving & & \\ \cline{1-1}  & & & speed, maximum & & \\ \cline{1-1}  & & & wind speed, radius of & & \\ \cline{1-1}  & & & the strong wind & & \\ \cline{1-1}  & & & speeds & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Sample of papers reviewed. Predictors, data types, and metrics used in each paper can be easily referenced in the table. The study by [23] developed a multioutput artificial neural network model which was used to predict storm surges in the North Carolinian coast. The authors mention a couple limitations of ANNs; for example, they found out that ANNs often underestimate peak surges. Furthermore, they concluded that the underestimations could be a result of the memoryless approach of ANNs. Naturally, including memory in an ANN-based machine learning model could help improve the results. The usage of memory in neural networks for tidal prediction was explored by [21], where they compared many approaches for predicting ocean water levels at 17 different stations in Taiwan. The model utilized a type of neural network called Long-short Term Memory (LSTM). LSTMs provide a solution to the memoryless problem mentioned by [23] and outperformed other methods. The results of LSTM show their potential for usage in storm surge prediction. It is important to mention that the study in [21] focused only on tidal levels, and no storm surge or weather conditions were considered. LSTMs are an example of Recurrent Neural Networks (RNNs). These types of neural networks are often used for process control or time series predictions [24].