An empirical analysis of hyperparameter tuning impact on ensemble machine learning algorithm for earthquake damage prediction

Earthquake damage prediction is crucial for ensuring the safety of building occupants and preventing substantial financial losses. Because it enables robust structural design, financial readiness, and well-timed expenditures in preventive measures, anticipating seismic impacts promotes sustainability and long-term building. Machine learning (ML) have transformed building damage prediction, providing efficient methodologies for assessing structural vulnerabilities and risks. ML analyzes multifaceted datasets, handling complex spatial and temporal data, enhancing accuracy in forecasting damage probabilities and enabling proactive monitoring for timely interventions. However, ensemble machine learning and the fine-tuning of such algorithms with the hyperparameter optimization with the earthquake damage prediction have not been explored in the literature yet. Hyperparameter optimization in machine learning enhances model performance and generalization capacity. Skillful adjustment of hyperparameters significantly improves predictive accuracy, resilience, and training convergence, ensuring optimal model performance across diverse datasets and real-world scenarios. This study focuses on improving earthquake damage prediction accuracy through an extensive analysis of the earthquake dataset on ensemble machine learning with hyperparameter tuning. Utilizing various hyperparameter tuning algorithms and examining five ensemble machine learning algorithms, combined with six distinct hyperparameter tuning techniques, significantly enhanced accuracy. The paper’s main contributions include exploring novel hyperparameter tuning algorithms for earthquake damage prediction and filling a knowledge gap in the field. The thorough dataset analysis revealed a scarcity of existing literature, suggesting opportunities for further research. The study underscores the critical role of hyperparameter analysis in machine learning and proposes potential applications beyond earthquake prediction, particularly in climate change.