Early Warning Tsunami Prediction Using Neural Networks: A Case Study in Vancouver Island, Canada

Tsunamis are one of the most devastating natural hazards, with the potential to cause extensive loss of life, property damage and socioeconomic disruptions. Developing robust and accurate early warning systems is critical to mitigating these impacts. In this study, a neural network-based early warning system is proposed to predict tsunami wave heights nearshore, focusing on the Vancouver Island area on the western coast of Canada. 

 

The Vancouver Island region, which is extremely susceptible to tsunami hazards because of its closeness to the Cascadia Subduction Zone, is the area used to generate the synthetic data. In tsunami research, synthetic data are essential because they enable the investigation of a variety of possible earthquake and tsunami scenarios, including uncommon but highly consequential occurrences. The dataset, which contains 5000 simulation scenarios, used includes parameters such as fault slip parameters, bathymetry, hypocenter position, and earthquake magnitude, as well as the related tsunami wave heights at particular nearshore locations. The parameters used to train the model are the maximum wave heights off shore at different stations and the parameter that the model is trained to predict is the maximum wave height near shore in different depth zones (0 m, 5 m, 10 m, and 100 m).

 

The neural network architecture was designed to model the nonlinear relationships between input parameters (maximum wave heights off shore at different stations) and resulting tsunami wave heights (near shore at different depths). By training, validating, and testing the neural network, the model demonstrated a high level of accuracy in predicting wave heights nearshore. The performance metrics, including mean absolute error and correlation coefficients, indicate that the neural network effectively captures the complexities of tsunami wave dynamics, making it suitable for early warning applications. According to the results, the neural network can accurately forecast tsunami heights close to shore, facilitating prompt evacuation preparation and disaster relief. This method is a major improvement over conventional physics-based models, which frequently demand a large amount of time and resources, by providing a computationally effective and scalable solution. Overall, this study demonstrates how machine learning, and in particular neural networks, might improve early warning systems for tsunamis.