Research on Earthquake and Tsunami Risk Prediction Models Based on Ensemble Learning

This article introduces an open-source project on GitHub that uses ensemble learning methods to build earthquake and tsunami risk prediction models. It discusses the application potential and technical implementation path of multi-model fusion in natural disaster early warning, aiming to improve prediction accuracy and stability, provide new ideas for the development of disaster warning technology, and is of great significance for reducing casualties and property losses.

Earthquake and tsunami prediction has long faced technical challenges, and traditional geological monitoring methods have limitations in accuracy and timeliness. With the development of machine learning technology, data-driven prediction methods have become a hot topic—potential disaster patterns can be identified by analyzing historical earthquake data, geological structure characteristics, and ocean monitoring data. The core goal of the project is to build a reliable system that integrates multiple data sources to quantify risks, which has both academic value and significance for the technological progress of public safety.

The project uses ensemble learning as the core technical route. Compared with a single model, combining multiple base learners can significantly improve generalization ability and prediction stability. In disaster prediction scenarios, due to the high dimensionality and complex nonlinear characteristics of data, a single model is difficult to capture all patterns; different base models are sensitive to different aspects of data, and ensemble strategies (such as Bagging, Boosting, Stacking) can integrate the strengths of each.

Earthquake prediction needs to process multi-source heterogeneous data: geological data includes fault distribution, crustal stress accumulation, historical earthquake frequency, etc.; ocean data involves seabed topography, tidal changes, seawater temperature anomalies, etc., which form model input vectors after preprocessing. Tsunami prediction relies more on earthquake parameters (magnitude, focal depth, epicenter location) and seabed topography data, combining physical models and machine learning to simulate wave propagation.

Multiple challenges are faced in implementation: In terms of data quality, historical earthquake data has incomplete records and measurement errors, requiring careful cleaning and missing value handling; for class imbalance issues, disaster events are low-probability events with far fewer positive samples than negative samples, so oversampling, undersampling, or cost-sensitive learning strategies need to be adopted; model interpretability needs to meet the needs of domain experts for understanding and verification, and decision-making basis can be revealed through feature importance analysis and SHAP values.

Currently, the model is more of a risk assessment tool, outputting a probability distribution to indicate the possibility of disaster occurrence rather than a deterministic forecast. The value of the project lies in providing a benchmark framework—more advanced deep learning architectures can be introduced later, real-time monitoring data streams can be integrated, or hybrid prediction can be combined with physical simulation models; collaboration in the open-source community will accelerate technical iteration.

Earthquake and tsunami prediction is a high-difficulty scenario for artificial intelligence applications, and ensemble learning methods provide a feasible path. This open-source project is a beneficial exploration of machine learning in the field of public safety, and its technical solutions and implementation ideas are worthy of researchers' attention. With the improvement of data quality and the evolution of algorithms, data-driven disaster warning systems are expected to play a greater role in the future.