Landslide susceptibility mapping based on the reliability of landslide and non-landslide sample

Spatial data sampling can improve the performance in geo-spatial prediction. However, measuring the reliability of polygon-based data in sampling process is still a challenge. In this study, a reliability-based sampling (RBS) method was proposed to deal with this question and it was applied in landslide susceptibility mapping. First, the prototype of landslide was extracted from landslide polygon data, then, the reliability of landslide samples and non-landslide samples is measured using the similarity in environmental factor between the candidate samples and the prototype. The mutual exclusion reliability threshold setting method is used to collect the landslide samples and non-landslide samples with reliability over certain threshold. A case study demonstrates that the RBS method is better than existing representative method (i.e. Landslide entity) in terms of Accuracy and AUC with different sample sizes. In summary, The RBS is an efficient method to improve the spatial pattern of samples can also be applied to in other geo-spatial predictions.