Implementation of the PSO-SMOTE Method on the Naive Bayes Algorithm to Address Class Imbalance in Landslide Disaster Data

Abstract

Landslides in Samarinda, which often occur after floods, pose a threat to settlements, infrastructure, and the agricultural sector. This study proposes a combination of Naïve Bayes, SMOTE (Synthetic Minority Oversampling Technique), and PSO (Particle Swarm Optimization) to address class imbalance in landslide prediction. The results show that while PSO successfully improves the accuracy of the Naïve Bayes model, the application of SMOTE led to a decrease in accuracy for some method combinations. This decrease is due to changes in data distribution caused by synthetic data, which can introduce noise and affect feature selection and model optimisation. However, the combination of Naïve Bayes with PSO optimisation resulted in a modest accuracy improvement (+0.48%). These findings suggest that SMOTE should be used cautiously, while PSO is more effective in enhancing the accuracy of the landslide prediction model. The implications for practical application are that although SMOTE and PSO can improve accuracy, the impact of synthetic data on data distribution must be considered, and further testing is needed to ensure its effectiveness in real-world conditions.