Landslides are one of the most critical categories of natural disasters worldwide and induce severely destructive outcomes to human life and the overall economic system. To reduce its negative effects, landslides prevention has become an urgent task, which includes investigating landslide-related information and predicting potential landslides. Machine learning is a state-of-the-art analytics tool that has been widely used in landslides prevention. This paper presents a comprehensive survey of relevant research on machine learning applied in landslides prevention, mainly focusing on (1) landslides detection based on images, (2) landslides susceptibility assessment, and (3) the development of landslide warning systems. Moreover, this paper discusses the current challenges and potential opportunities in the application of machine learning algorithms for landslides prevention.

Geologic hazards (Geo-hazards) are naturally occurring or human-activity induced geologic conditions capable of causing damage or loss of property and/or life. Geo-hazards such as landslides, surface subsidence, and earthquakes can seriously affect and threaten life, property, or public safety. Geo-hazards prevention is the application of geologic engineering principles and existing and emerging technologies to reduce, minimize or prevent the effects of various geologic hazards. Monitoring and early-warning are the most common strategies for geo-hazards prevention. With the development of the Internet of Things (IoT), an emerging new idea is to apply IoT technology to enhance the accuracy and efficiency of monitoring and early-warning systems for geo-hazards prevention. This paper aims to present a comprehensive survey of relevant research and technological developments of the IoT applied in geo-hazards prevention. It first surveys the applications of the IoT in the monitoring and early-warning of seven types of common geo-hazards, including landslides, debris flow, rockfall, surface subsidence, surface collapse, surface cracks, and earthquakes, then investigates the key technologies in geo-hazards prevention when utilizing the IoT, and finally summarizes the challenges in IoT-based monitoring and early-warning systems for geo-hazards prevention. Moreover, this paper also highlights the future directions for employing the IoT for geo-hazards prevention.

Landslides are a common type of natural disaster in mountainous areas. As a result of the comprehensive influences of geology, geomorphology and climatic conditions, the susceptibility to landslide hazards in mountainous areas shows obvious regionalism. The evaluation of regional landslide susceptibility can help reduce the risk to the lives of mountain residents. In this paper, the Shannon entropy theory, a fuzzy comprehensive method and an analytic hierarchy process (AHP) have been used to demonstrate a variable type of weighting for landslide susceptibility evaluation modeling, combining subjective and objective weights. Further, based on a single factor sensitivity analysis, we established a strict criterion for landslide susceptibility assessments. Eight influencing factors have been selected for the study of Zhen’an County, Shan’xi Province: the lithology, relief amplitude, slope, aspect, slope morphology, altitude, annual mean rainfall and distance to the river. In order to verify the advantages of the proposed method, the landslide index, prediction accuracy P, the R-index and the area under the curve were used in this paper. The results show that the proposed model of landslide hazard susceptibility can help to produce more objective and accurate landslide susceptibility maps, which not only take advantage of the information from the original data, but also reflect an expert’s knowledge and the opinions of decision-makers.