The Study of Using Fuzzy Logic for Improving Landslide Early Warning Accuracy - MOUNIVONG, maleny; SAYPADITH, Savath (Souphanouvong University Journal Multidisciplinary Research and Development, 2025)
Landslides are natural disasters that occur in many regions worldwide, particularly in mountainous areas. These events pose serious threats to human life and property and are often triggered by factors such as intense or prolonged rainfall, steep slopes, deforestation, road construction, and other land-use changes. These conditions alter the geological stability of an area, increasing the risk of landslides. Due to the sudden and unpredictable nature of landslides, early detection and accurate warnings are critical to minimizing damage and saving lives. This study aims to enhance the efficiency of landslide early warning systems using Fuzzy Logic, combined with MATLAB and a Threshold-based Classification method. A total of 30 initial data sets were collected, including values for rainfall, land slope, soil moisture, vibration, and readings from top and bottom sensors. Risk levels were classified into three categories based on Fuzzy Logic analysis: Low Risk (Safe) – no immediate danger; Medium Risk (Alert) – possible landslide, early warning required; and High Risk (Danger) – landslide imminent, immediate action needed. The study compared the performance of a conventional Threshold-based Classification method with a Fuzzy Logic-based approach. Evaluation was conducted using the Confusion Matrix to assess accuracy, precision, recall, and F1-score. The results demonstrate that the Fuzzy Logic method significantly outperforms the traditional approach. The model achieved an accuracy of 90.0%, precision of 91.4%, recall of 90.0%, and an F1-score of 89.5%. The non-Fuzzy Logic method showed lower performance and misclassified data in cases with multiple risk levels. These findings indicate that integrating Fuzzy Logic improves the reliability of landslide early warning systems and supports better decision-making in high-risk areas.