Chaotic Chimp Based African Vulture Optimization Algorithm with Stability Tests for Feature Selection Algorithms

Earthquake prediction remains a major challenge in the field of geophysics, with significant implications for disaster management and risk reduction. Accurate prediction depends on identifying and selecting relevant features from large and complex datasets. In this study, we present a novel feature selection method, the Chaotic Chimp based African Vulture Optimization Algorithm (CCAVO), applied to earthquake magnitude prediction. The model was trained on a dataset containing various seismic event characteristics such as latitude, longitude, depth, and other geological factors. The target variable for prediction was the magnitude of the seismic event. We conducted three stability tests on the model: Convergence Rate, Consistency Test, and Sensitivity to Parameters. Our analysis revealed that the CCAVO demonstrated good convergence behavior, with training errors reducing over successive iterations, indicating the model’s ability to learn from the data. The consistency test further showed that the model performance, as quantified by the Mean Squared Error (MSE), remained consistent across multiple runs with different random seeds, suggesting the model’s stability and robustness against randomness in initialization. Finally, a sensitivity analysis was performed to examine the model’s response to changes in its hyperparameters. The model’s performance was observed to vary with different parameter settings, indicating its sensitivity to hyperparameters. The optimal parameters found were a learning rate of 0.1 and 100 estimators, yielding 0.08 MSE from 3-fold cross-validated MSE.