Abstract
Landslide susceptibility mapping (LSM) can provide valuable information for local governments in landslide prevention and mitigation. Despite significant improvements in the predictive performance of LSM, it remains a challenge to be carried out in areas with limited availability of data. For example, in the early stage of road construction, landslide inventory data can be particularly scarce, while there is a high need to have a susceptibility map. This study aims to set up a novel procedure for coupling the knowledge-driven and data-driven models for LSM in an area with limited landslide inventory data. In particular, we propose a two-step approach. The first step consists of applying four data-driven models (logistic regression, decision tree, support vector machines, and random forest (RF)) to derive a regional susceptibility map. In the second step, the application of a heuristic model (analytic hierarchy process, AHP) is proposed to calculate a local susceptibility map for the areas with incomplete landslide inventories. The final landslide susceptibility map is obtained by merging the most accurate regional map (RF) with the local map. We apply this novel procedure to a landslide-prone region with developed road construction (National Highway G69) in Wanzhou district, where landslide inventory is difficult to update due to timely recovery from landslide-induced road damage. Results show that the proposed methodology allows identifying new landslide-prone areas, and improving LSM predictive performance, as demonstrated by the fact that two new landslides developed along G69 were perfectly classified in the highly susceptible areas. The results show that implementing the landslide susceptibility assessment with different geographical settings and combining them into best-sensitivity partitions is more accurate than focusing on creating new models or hybrid models.
Highlights
In the study area of Wanzhou country, we explored the performance of four data-driven models (LR, decision tree (DT), support vector machine (SVM), random forest (RF)) and compared them with the analytic hierarchy process (AHP) model
The results show that the proportion of the very-high-susceptibility class has increased by 2.4%, while the proportions of the low, moderate, and high-susceptibility class have decreased by 2.24%, 0.25%, and 0.14%, respectively
The data-driven approach has been applied to derive a first regional landslide susceptibility map valid for the whole area, except in the nearby of the road
Summary
Landslides pose great threats to human lives and economic development around the world [1]. The mountainous areas of southwestern China suffer a lot from landslide hazards. The Shuicheng landslide-debris occurred in Guizhou Province, China, resulting in 21 buildings being buried, 9 missing people, and 42 deaths in 2019 [2]. Another catastrophic landslide in Jiweishan Mountain, Chongqing province, China, caused
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