Classification of food security status in disaster-prone areas: a comparative analysis of machine learning models using Support Vector Machine, Random Forest, and Logistic Regression.

Food security assessment in disaster-prone areas requires accurate and adaptive approaches capable of capturing dynamic production-consumption relationships. This study aims to develop and evaluate machine learning-based classification models for determining regional food security status using a Food Security Index (FSI), defined as the ratio of local food production to annual consumption requirements with an added safety buffer. Three algorithms were compared: Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression (LR). Model performance was evaluated using k-fold cross-validation and assessed through accuracy, precision, recall, F1-score, and confusion matrix analysis. The results show that SVM achieved the highest and most consistent performance, with accuracy ranging from 92% to 96%, precision of 0.93, recall of 0.94, and an F1-score of 0.93. Random Forest demonstrated moderately strong performance (accuracy: 88-92%; F1-score: 0.87), while Logistic Regression exhibited the lowest performance (accuracy: 80-85%; F1-score: 0.80), indicating its limitation in modeling non-linear relationships. Confusion matrix analysis reveals that SVM maintains stable classification across all validation folds with minimal misclassification, whereas errors in all models are predominantly concentrated in regions with FSI values near the classification threshold (FSI ≈ 1), highlighting the challenge of boundary-sensitive cases. Substantively, the integration of production-consumption ratios with buffer reserves in the FSI effectively captures both food availability and system resilience, distinguishing surplus regions (FSI ≥ 1) from structurally vulnerable regions (FSI < 1). These findings confirm that SVM is the most suitable approach for food security classification in disaster-prone areas due to its ability to model non-linear patterns and optimize decision boundaries. This study provides a robust and scalable framework for data-driven food security monitoring and early warning systems. The results support the use of SVM-based classification to enhance the accuracy of vulnerability detection and to improve the targeting of policy interventions in disaster-prone regions.