Statistical and machine learning methods for multi-step earthquake frequency forecasting in indonesian regions

Abstract Despite the devastating impact of earthquakes, they offer potential for machine learning prediction to mitigate damage. This study explores the application of common algorithms like Random Forests, Support Vector Machines (SVMs), XGBoost, and Long Short-Term Memory (LSTM) networks alongside the Autoregressive Integrated Moving Average (ARIMA) framework for earthquake frequency forecasting in Indonesian regions. A novel hybrid model combining machine learning with ARIMA for multi-step forecasting is introduced. Surprisingly, the LSTM model, renowned for its strong predictive capability in nonlinear relationships, performed significantly lower than traditional machine learning methods based on metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results highlight the superior predictive capability of the hybrid ARIMA-XGBoost and ARIMA-RandomForest models in multi-step forecasting. These findings underscore the continued relevance and effectiveness of traditional machine learning approaches in earthquake data prediction, suggesting avenues for future research to refine hybrid models and improve multi-step regression forecasting accuracy.