| Citation: | HUANG Faming，CHEN Jie，YANG Yang，et al. A review and prospect of research on disaster-causing environment factors related to landslide susceptibility prediction[J]. Bulletin of Geological Science and Technology，2025，44（2）：1-24 doi: 10.19509/j.cnki.dzkq.tb20240766
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Disaster-causing environmental factors serve as input variables for landslide susceptibility prediction modeling, referring to various natural attribute factors influencing the occurrence, development, and distribution of landslides on slopes. A comprehensive and clearly-defined set of disaster-causing environmental factors is of vital importance for enhancing the accuracy and reliability of landslide susceptibility outcomes.
To further clarify the research status and future prospects of disaster-causing environmental factors, this paper conducted a literature search in the core collection database of Web of Science, with the titles containing "landslide susceptibility" and the publication date ranging from 01/01/2013 to 31/12/2023, collecting 767 English papers on landslide susceptibility to form a literature database. Firstly, information such as the quantity of disaster-causing environmental factors, acquisition methods, sources, importance, and acceptance in each paper was statistically analyzed. Then, the definitions and physical meanings of disaster-causing environmental factors were elaborated in detail. Subsequently, characteristics such as the optimization selection/combination methods of disaster-causing environmental factors, factor connection methods, factor errors, and suitability were discussed, providing a reference for the uncertainty research of selecting disaster-causing environmental factors when predicting landslide susceptibility.
The review results indicate that: (1) A total of 82 types of disaster-causing environmental factors were statistically analyzed in the literature database, with over 40 frequently used ones. Among them, slope, aspect, elevation, and lithology are the four most frequently used factors. The importance of factors such as slope, elevation, road density, lithology, and rainfall in landslide susceptibility prediction is the highest in sequence. (2) It was discovered that research on using comprehensive and physically meaningful disaster-causing environmental factors, constructing model input variables based on environmental factor connection methods, eliminating random errors in environmental factors, enhancing the suitability of environmental factors, and employing various explainable methods can effectively improve the performance of machine learning in predicting landslide susceptibility. Therefore, in future research on the disaster-causing environmental factors of landslides, it is necessary to focus on these key issues.
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