Land subsidence susceptibility modeling using random forest approach (Case study: Tasuj plane catchment)

Land subsidence occurrence in the Tasuj plane might become more frequent and hazardous in the near future due to its relationship with the water crisis and drought periods. In order to mitigate the damage caused by land subsidence, it is necessary to determine the susceptible or prone areas. The purpose of this study is to produce land subsidence susceptibility map based on the random forest approach to land subsidence occurrence data and eleven environmental variables  that have significant influence on land subsidence occurrences (altitude, slope, aspect, distance to drainage line, drainage density, distance from the fault, topographic wetness index, land cover, lithology, groundwater level and decline in groundwater level) were used as inputs of the random forest model. The random forest approach was applied to produce the land subsidence susceptibility map. The performance of the model was assessed using the receiver operating characteristics (ROC) curve and the area under the curve (AUC). The model results indicate the accuracy of 0.86. Based on the result of the mean decrease accuracy method, the most important conditioning factors were groundwater level, distance from the fault, and a decline in groundwater level, respectively. According to the result, about 18% and 11% of the study area was located within high to very high susceptibility classes. The result of this study can be used by stakeholders and local authorities to mitigate related hazards of land subsidence occurrences in the study area.