Evaluating landslide susceptibility: an AHP method-based approach enhanced with optimized random forest modeling

Understanding the extent of landslide damage is important for reducing the impact of landslides, which can cause great losses of life and property. Although numerous studies have been done on landslide disaster susceptibility, they have been limited by an unreasonable negative sample selection strategy or the absence of subjective environmental information of the study area in a single machine learning evaluation model. To evaluate landslide susceptibility based on sample optimization, we propose an analytic hierarchy process (AHP) method weighted by an improved random forest (RF) model. Based on the density analysis of landslide data, this method employs the certainty factor (CF) method to generate negative sample data. Correspondingly, ADB_RF, an enhanced RF model based on adaptive boosting (AdaBoost) is proposed to obtain objective weights, which are then combined with subjective weights obtained by the AHP (CF-combination). Additionally, a case study on the evaluation of landslide disasters was conducted in the Chuxiong Autonomous Prefecture of Yunnan, China. The results show the following: (1) the proposed landslide susceptibility evaluation method could objectively reflect the area prone to landslides with a high degree of accuracy and efficacy. (2) The area under the curve (AUC) of the CF-combination model reached 96.1%, indicating a high degree of accuracy. (3) In the northwestern region of Chuxiong Prefecture, more extremely high-risk areas were found than in the southeast; therefore, it has a high likelihood of experiencing another landslide disaster, which requires special attention. Accordingly, the research findings have significant reference value for preventing disasters and mitigating losses.