Evaluation of the susceptibility to landslide geological disasters based on different slope units and an information content random forest model: a case study of the Longhua District, Shenzhen

Slope units are divided according to the real topography and have clear geological characteristics, making them ideal units for evaluating the susceptibility to geological disasters. Based on the results of automatically and manually corrected hydrological slope unit division,the Longhua District, Shenzhen City, Guangdong Province, was selected as the study area. A total of 15 influencing factors,namely Fluctuation,slope, slope aspect, curvature, topographic witness index (TWI),stream power index (SPI),topographic roughness index (TRI), annual average rainfall,distance to water system, engineering rock group,distance to fault, land use,normalized difference vegetation index (NDVI),nighttime light, and distance to road, were selected as evaluation indicators. The information volume model (Ⅳ) and random points were used to select non-geological disaster units, and then the random forest model (RF) was used to evaluate the susceptibility to geological disasters. The automatic slope unit and the hydrological slope unit were compared and analyzed in the random forest and information volume random forest models. The results show that the area under the curve (AUC) values of the automatic slope unit evaluation results are 0.931 for the Ⅳ-RF model and 0.716 for the RF model, which are 0.6% (Ⅳ-RF model) and 1.9% (RF model) higher than those for the hydrological slope unit. Based on a comparison of the evaluation methods based on the two types of slope units, the hydrological slope unit evaluation method based on manual correction is highly subjective, is complicated to operate,and has a low evaluation accuracy, whereas the evaluation method based on automatic slope unit division is efficient and accurate, is suitable for large-scale efficient geological disaster evaluation, and can better deal with the problem of geological disaster susceptibility evaluation.