Development of a Machine Learning Model for Real-Time Earthquake Hazard Prediction

Thesis Abstract

Earthquake hazards pose significant threats to urban populations and infrastructure, necessitating the development of predictive systems capable of providing timely warnings to mitigate potential damages. Despite advances in seismology, existing earthquake prediction methods often lack the capacity for real-time hazard forecasting, primarily due to limitations in processing complex seismic data and extracting actionable insights swiftly. This study aims to develop a robust machine learning model capable of real-time earthquake hazard prediction by leveraging seismic, geophysical, and environmental datasets. The specific objectives are to (1) compile an extensive dataset of seismic activity, ground motion parameters, and environmental variables from a network of 50 seismic stations across the study region over a five-year period; (2) evaluate and compare multiple machine learning algorithms—including Random Forest, Support Vector Machines, Gradient Boosting, and Deep Neural Networks—for their efficacy in predicting imminent earthquake hazards; (3) identify the most relevant features contributing to accurate hazard forecasts using feature importance and recursive feature elimination techniques; and (4) design an operational framework for deploying the predictive model in real-time alert systems. The research adopts a quantitative, exploratory research design utilizing secondary data and primary data collection through seismic sensors. The population consists of seismic activity records from regional seismic stations supplemented by environmental sensor data. A stratified random sampling technique is employed to select 10% of recorded seismic events for detailed analysis, resulting in a dataset of approximately 10,000 events. Data collection instruments include digital seismometers and environmental sensors installed specifically for this research, alongside existing geological databases. Data preprocessing involves normalization, handling missing values, and feature engineering to enhance model performance. The analytical framework centers on supervised machine learning algorithms evaluated via cross-validation, with model performance assessed using metrics such as accuracy, precision, recall, and F1-score. Model optimization is performed through hyperparameter tuning using grid search and Bayesian optimization techniques. Expected findings suggest that ensemble algorithms like Gradient Boosting and Deep Neural Networks will outperform others in predicting imminent seismic hazards with high statistical significance, characterized by an expected accuracy of over 85%, and a reduction in false-positive alerts by approximately 20% compared to baseline models. The process of feature selection is anticipated to highlight critical predictors such as seismic wave amplitude, frequency domain features, and environmental stress indicators. These results are expected to demonstrate the potential for machine learning models to significantly enhance early warning systems, enabling authorities to implement timely evacuations and disaster preparedness measures. This research contributes to existing knowledge by providing a comprehensive model integrating multi-source data streams and advanced algorithms for real-time earthquake hazard prediction, filling gaps identified in prior studies regarding the operational deployment of predictive models in emergency scenarios. Furthermore, it advances the theoretical understanding of seismic data patterns and their relationship with environmental variables, supported by the application of the Stress Accumulation Theory and the Earthquake Nucleation Model. The developed framework aims to catalyze the integration of artificial intelligence into seismic hazard management systems, promoting more resilient urban planning and disaster risk reduction strategies. The main conclusion underscores the viability and superiority of machine learning approaches for earthquake hazard prediction, emphasizing their capability to deliver timely, accurate alerts that can save lives and reduce economic losses. Recommendations include scaling the model for larger geographic regions, integrating it with existing early warning infrastructure, and conducting further research into improving model interpretability and robustness under varied seismic conditions. Future studies should explore the integration of real-time social and infrastructural data to enhance predictive accuracy further. The findings are expected to influence policy formulation in seismic risk management and foster the adoption of AI-driven solutions across geoscientific and disaster response communities.