Integrating Computational Wisdom

Abstract

This study employs deep learning and machine learning to critically analyze the significance of ensemble approaches in earthquake prediction. It examines research that has utilized ensemble techniques to enhance prediction resilience, emphasizing the value of boosting, bagging, and random forests. The analysis includes a discussion of the impacts of hyperparameter tuning, ensemble diversity, and the combination of multiple models. The chapter highlights the growing trend of merging seismic and non-seismic precursors to improve forecasting capabilities. Advances in data integration techniques and interdisciplinary models are reviewed, along with multidisciplinary approaches that incorporate numerous sources of data. This chapter focuses on the development and deployment of early warning systems that use both seismic and non-seismic data to predict earthquakes. It also suggests the need for standardized data formats and cooperative efforts to overcome difficulties in data integration and standardization.