A review paper on Landslide Susceptibility Mapping using Geospatial Technology and Machine Learning Techniques

Landslides are among the most frequent and devastating natural hazards, often resulting in significant loss of life, property damage and disruption to infrastructure and agriculture. As a serious geo-environmental issue, landslides present complex challenges for both prediction and control. Landslide Susceptibility Mapping (LSM) has emerged as a valuable tool for identifying high-risk areas and supporting disaster mitigation strategies. In recent years, numerous researchers have applied geospatial technologies in combination with statistical methods and machine learning techniques to enhance the accuracy of LSM. Review papers play a crucial role in helping researchers and academicians to identify knowledge gaps and to evaluate existing methodologies by synthesizing findings from previous studies. This review is based on a comprehensive collection of research studies focused on LSM using geospatial and machine learning approaches, aiming to provide insights into current practices and future research directions. The analysis reveals that machine learning models, particularly Random Forest (RF), Support Vector Machine (SVM) and Gradient Boosting Decision Trees (GBDT), consistently outperform traditional statistical methods like Logistic Regression (LR) and Frequency Ratio (FR) in predictive accuracy. Studies have reported AUC values exceeding 0.95 for RF models, indicating excellent predictive capabilities in various geographical contexts. Furthermore, the integration of Bayesian optimization techniques has enhanced model performance, with improvements in prediction accuracy up to 7% for GBDT models. Hybrid models, combining algorithms such as SVM with metaheuristic optimization methods, have also demonstrated superior performance, effectively capturing complex, nonlinear relationships inherent in geospatial data. In conclusion, the adoption of advanced machine learning and hybrid models has significantly improved the accuracy and reliability of LSM. These methodologies offer robust tools for disaster risk management, enabling more effective identification of high-risk areas and informing mitigation strategies. Future research should focus on enhancing model interpretability and integrating real-time data to further refine susceptibility assessments and support proactive landslide risk reduction efforts.