Seismic landslide susceptibility assessment based on the SCM-ANFIS model: A case study of the Wenchuan earthquake area

The assessment of landslide susceptibility triggered by earthquakes serves as a fundamental basis for effective emergency response and post-disaster reconstruction efforts. However, current predictive models often face limitations in accuracy, with the prediction rates of most models ranging from 80% to 90%. This study introduces a new hybrid machine learning framework, termed the Subtractive Clustering Method-based Adaptive Neural Network Fuzzy Inference System (SCM-ANFIS), and evaluates its performance in the Wenchuan earthquake region. This region features distinctive geology (e.g., Longmenshan Fault-governed complex tectonics) and abundant fundamental data; additionally, the 2008 Wenchuan Earthquake provides a pertinent case for earthquake-induced landslide model evaluation. Based on a literature review and correlation analysis, this study systematically identified 12 key influencing factors that collectively characterize the region's high landslide susceptibility, shaped by intense seismic activity, complex terrain, and fragmented rock masses. Positive and negative samples were extracted as target variables through buffer sampling to calculate earthquake-induced landslide susceptibility. The susceptibility zoning map was then calibrated and generated by incorporating the regional landslide area percentage. The study concludes the following: (1) Compared to traditional machine learning approaches, the model demonstrates strong performance and stability, achieving a prediction accuracy of 98.5%. Approximately 97.89% of historically documented landslides in the Wenchuan region were located within areas identified as having high susceptibility, which aligns well with observed spatial distributions. (2) Increase in the buffer distance contributes to enhance prediction accuracy while a larger sample size improves model stability. (3) The model exhibits superior performance and possesses scalability for application in other regions, such as Jiuzhaigou and Luding. (4) Nonetheless, limitations remain regarding uncertainty, sample composition, algorithmic design, and practical implementation. Future research should focus on improving data precision and optimizing algorithmic frameworks. Overall, this study provides valuable support for landslide susceptibility assessments and contributes essential data for disaster risk mitigation efforts.