Influences of non-landslide sample selection strategies on landslide susceptibility mapping by machine learning

Landslide susceptibility mapping is crucial in mitigating the risk of regional landslide hazards. The main objectives of this study are to design and analyze the impact of three non-landslide data generation methods on landslide susceptibility mapping. The specific non-landslide data selection methods are the random generation of non-landslide points through different distance landslide buffers, by selecting areas with low landslide influence in the landslide condition factor layer, and based on the results of the information value model partitioning by selecting low to medium susceptibility zones. There were 14 landslide condition factors used for the landslide susceptibility mapping, and correlations between landslides and condition factors were analyzed using the Gini index. The 70% dataset was modelled using a logistic regression model as well as an artificial neural network model. Finally, statistical metrics and AUC values were used for the 30% validation data for comparing the predictive performances of different non-landslide data generation strategies in the model. Based on the validation results, models A6 and B6 performed the best, with respective AUC values of 0.997 and 0.995. The findings show that the non-landslide samples generated using the low susceptibility interval of the information value model have the best performance.