Optimizing Earthquake Prediction Accuracy using Somersaulting
Spider Optimizer for Dynamic Ensemble Weighting
Ahmed Mohamed Zaki1,*, Hala B. Nafea1, Hossam El-Din Moustafa1,2, El-Sayed M. El-Kenawy3,4,*
1 Department of Electronics and Communications Engineering, Faculty of Engineering, Mansoura
University, Mansoura, 35516, Egypt
2 Faculty of Artificial Intelligence and Informatics, Horus University, New Damietta, 34517, Egypt
3 Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology,
Mansoura, 35111, Egypt
4 Applied Science Research Center, Applied Science Private University, Amman, Jordan
Emails: ahmeduzaki@std.mans.edu.eg ,halabahyeldeen@mans.edu.eg
hossammoustafa@mans.edu.eg, skenawy@ieee.org
Abstract
Earthquake prediction is one of the most challenging problems in geophysical science, and
conventional approaches have proven arduous in capturing the complexity and non-linearity of
seismic measurements. The multidimensional nature of earthquake variability, along with class
imbalance and the strong dependence of prediction results on hyperparameters, necessitates the
development of more robust and flexible predictive models. In this paper, we introduce a bio-inspired
ensemble learning method based on the Somersaulting Spider Optimizer (SSO) for dynamically
adjusting classifier weights in earthquake classification. The proposed method addresses limitations
of existing weighting strategies, which primarily focus on maximizing classifier contribution based on
performance characteristics. Experiments were conducted on an earthquake dataset augmented with
features modeled and mapped by time, space, and magnitude to capture patterns of seismic events.
We compared the SSO-optimized ensemble with BaggingClassifier, CatBoost, HistGradientBoosting,
LightGBM, and DecisionTree, as well as traditional ensemble approaches. Results show that the
SSO-boosted ensemble achieved superior performance, with an accuracy of 97.01%, sensitivity of
97.04%, specificity of 99.36%, precision of 97.64%, and an F1-score of 97.33%, outperforming other
models and traditional ensembles. These improvements were confirmed statistically using Wilcoxon
signed-rank tests, while visual analyses demonstrated enhanced stability and generalization. Overall,
the integration of bio-inspired optimization and ensemble learning shows strong potential to overcome
challenges in earthquake forecasting and to support reliable early warning and disaster preparedness
systems.
Received: June 5, 2025 Revised: July 28, 2025 Accepted: September 10, 2025
Keywords: Earthquake prediction; Ensemble learning; Somersaulting spider optimizer; Bioinspired
optimization; Seismic classification; Machine learning