Landslide Susceptibility Mapping Methods Coupling with Statistical Methods, Machine Learning Models and Clustering Algorithms

Landslides frequently occur in the mountainous areas of western China. Accurate mapping of landslide susceptibility is essential for geohazard management. Integrated models combining statistical methods and machine learning models have been widely applied to landslide susceptibility mapping. However, further optimization of their results is still worth investigation. This study proposes a comprehensive assessment method that couples statistical methods, machine learning models, and clustering algorithms. The effectiveness of the proposed method on improving the accuracy of landslide susceptibility mapping in Ningnan County is investigated. Firstly, the landslide influencing factors are selected from five aspects: geological environment, topography and geomorphology, meteorology and hydrology, vegetation and soil, and human engineering activities in the study area. Indicators are initially selected based on correlation analysis using the Pearson correlation coefficient method, and highly correlated factors are eliminated to establish the landslide susceptibility mapping index system. Next, the Information Value (IV), Certainty Factor (CF), and Frequency Ratio (FR) methods are combined with Random Forest (RF) model respectively to obtain three integrated models (IV-RF, CF-RF, and FR-RF). Then, the ISO clustering algorithm, Natural Breaks clustering, and Kmeans clustering algorithms are introduced to classify the results of the three integrated models, obtaining nine coupled assessment models (IV-RF-ISO, CF-RF-ISO, FR-RF-ISO, IV-RF-NBC, CF-RF-NBC, FR-RF-NBC, IV-RF-Kmeans, CF-RF-Kmeans, and FR-RF-Kmeans). Lastly, Area Under the Curve value (AUC), accuracy, F1 score, and Seed Cell Area Indexes (SCAI) are used to evaluate the accuracy of the models. The results demonstrate that all the integrated models outperform single models. The accuracy and F1 score of all integrated models both exceed 0.85, and their AUC values exceed 0.9. The integrated models effectively address the misclassification of non-landslide samples, which is especially prominent in single IV and CF models. Among the integrated models, the FR-RF model performs the best. The accuracy (0.911), F1 score (0.912), and AUC value (0.965) of FR-RF model improves by 0.095, 0.096, and 0.074, respectively, compared to the FR model. Compared with the natural break and Kmeans clustering methods, the coupled FR-RF-ISO model exhibits the optimal classification results, and the difference in SCAI values between its high and low susceptibility zones is more significant. The extremely high landslide susceptibility zones are primarily concentrated in the southern, eastern, and central parts of Ningnan County. The study demonstrates the high accuracy of the integrated assessment method that couples statistical methods, machine learning, and clustering algorithms, and provides insights for improving the accuracy of landslide susceptibility mapping. © 2024 Science Press. All rights reserved.