# Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre
## 4 Forecasting Model  
There are three forecasting models that were created during this study. The differences between them relies mainly on the machine learning architectures used and their coupling. The two types of architectures used in the models created are CNN, LSTM and CNN+LSTM. As explained before, CNNs are a type of artificial neural network architecture specialized in the analysis of image data. CNNs are inspired in the biology of the visual cortex of animals. They are great at extracting relevant features out of images and, given that the type of data used for the realization of this study can be interpreted as an image, the usage of CNNs suits the type of problem at hand. The first model created only considers image data as its input and produces a time series of ocean elevations corresponding to a specific  
Figure 2: Paths followed by Hurricane Alex, Dolly, and Hanna as they made landfall in Texas. The white line depicts the U.S.–Mexico border. time interval in the future. There are a total of 4 CNN-LSTM layers, with pooling operations in between them that accept the time series of images of future weather conditions. Their output is then fed to a series of regular densely connected neural network layers where the final time series output prediction is generated. Training parameters are the same across the CNN and LSTM models: 50 epochs, 6 samples per batch, and a validation split of 25%. The second model to be evaluated is based on the LSTM neural network architecture. It is composed of 50 LSTM units that connect to a series of regular neural network layers where the output is generated. The training parameters are the same as the CNN model above. The model will not utilize image data for its input; instead, it will use time series data corresponding to past conditions of water surface elevation. Using this information, the model will create a prediction of future conditions. The third model is a combination of both architectures and can be thought of as the final forecasting model. The two previously described models are simply a set of preliminary attempts to judge the capabilities of the architectures to establish a connection between past ocean surface elevations and weather and future ocean surface elevations. The final surrogate model combines both architectures to take more information into account for the generation of storm surge predictions. The model can be classified as a mixed-input model with two heads. The first head corresponds to the CNN model, using the same architecture discussed in the CNN-only model and accepting the same time series input of images. The second head of the model is the LSTM-only model, with the same architecture and input of time series elevations. Their outputs are then concatenated and fed to a series of densely connected neural network layers that produce the final output. This final model was trained on 100 epochs with a smaller batch size of 3 samples to accommodate the size of the model in the GPU. All three models were trained on the same hardware, an RTX 3060 NVIDIA GPU with 12 GB of VRAM. The training time for the CNN model took around an hour, while the LSTM model only took 5 min to train; however, the CNN+LSTM mixed-input model's total training time was around 7 h. Mixed precision was also leveraged to achieve speed ups. To train the respective models, two sets of data were utilized. First, a set to train the preliminary models to evaluate their performance based on data from a recording station. Second, a set to train the final surrogate model based on synthetic data generated with the ocean circulation numerical model. Both sets of data have corresponding water surface elevation and forecasted weather conditions data. The first, or preliminary training dataset, contains water surface elevation data coming from the Center for Operational Oceanographic Products and Services (CO-OPS) Port Isabel recording station in Texas, with ID: 8779770. The data obtained from the Port Isabel recording station has the same coverage as the forecasted weather information.