Multi-geohazards susceptibility mapping based on machine learning—a case study in Jiuzhaigou, China

• Published in: Natural hazards (Dordrecht) 2020-07, Vol.102 (3), p.851-871
• Main Authors: Cao, Juan, Zhang, Zhao, Du, Jie, Zhang, Liangliang, Song, Yun, Sun, Geng
• Format: Article
• Language: English
• Subjects: Civil Engineering; Debris flow; Detritus; Earth and Environmental Science; Earth Sciences; Emergency management; Environmental Management; Evolution; Flow mapping; Geographic information systems; Geological hazards; Geology; Geophysics/Geodesy; Geotechnical Engineering & Applied Earth Sciences; Hazard assessment; Hydrogeology; Information systems; Landslides; Landslides & mudslides; Learning algorithms; Machine learning; Methods; Mitigation; Natural Hazards; Original Paper; Plateaus; Prediction models; Probability theory; Rock falls; Rocks; Satellite data; Slope stability; Slopes; Support vector machines
• ISSN: 0921-030X; 1573-0840
• DOI: 10.1007/s11069-020-03927-8

Jiuzhaigou, located in the transitional area between the Qinghai–Tibet Plateau and the Sichuan Basin, is highly prone to geological hazards (e.g., rock fall, landslide, and debris flow). High-performance-based hazard prediction models, therefore, are urgently required to prevent related hazards and manage potential emergencies. Current researches mainly focus on susceptibility of single hazard but ignore that different types of geological hazards might occur simultaneously under a complex environment. Here, we firstly built a multi-geohazard inventory from 2000 to 2015 based on a geographical information system and used satellite data in Google earth and then chose twelve conditioning factors and three machine learning methods—random forest, support vector machine, and extreme gradient boosting (XGBoost)—to generate rock fall, landslide, and debris flow susceptibility maps. The results show that debris flow models presented the best prediction capabilities [area under the receiver operating characteristic curve (AUC 0.95)], followed by rock fall (AUC 0.94) and landslide (AUC 0.85). Additionally, XGBoost outperformed the other two methods with the highest AUC of 0.93. All three methods with AUC values larger than 0.84 suggest that these models have fairly good performance to assess geological hazards susceptibility. Finally, evolution index was constructed based on a joint probability of these three hazard models to predict the evolution tendency of 35 unstable slopes in Jiuzhaigou. The results show that these unstable slopes are likely to evolve into debris flows with a probability of 46%, followed by landslides (43%) and rock falls (29%). Higher susceptibility areas for geohazards were mainly located in the southeast and middle of Jiuzhaigou, implying geohazards prevention and mitigation measures should be taken there in near future.