机器学习方法在滑坡易发性评价中的应用

中国山区多、地形复杂,构造发育、地质灾害隐患分布广泛.滑坡作为山区最具灾难性的地质灾害之一,严重威胁着人民群众的生命及财产安全.构建滑坡易发性模型能够量化滑坡发生的可能性,对制定防灾措施、减少潜在风险具有重要作用.由于经验驱动模型难以量化,且往往依赖主观判断,近年来,滑坡易发性模型的精度与准确度在从经验驱动和统计理论模...

Machine learning algorithms and techniques for landslide susceptibility investigation: A literature review

There are many mountainous areas in China, with complex terrain, weak planes and geological structures and wide distribution of geohazards. Landslides are one of the most catastrophic natural hazards occurring in mountainous areas, leading to economic loss and casualties. Landslide susceptibility models are capable of quantifying the possibility of where landslides are prone to occur, which plays a significant role in formulating disaster prevention measures and mitigating future potential risk.Since expert-based models are difficult to quantify and generally depend on the subjective judgments, the accuracy and precision of landslide susceptibility models are now evolving from expert models and statistical learning toward the promising use of machine learning methods. This study presented critical reviews on current machine learning models for landslide susceptibility investigation, an extensive analysis and comparison between different machine learning techniques (MLTs) from case studies in the Three Gorges Reservoir area was presented. In combination with field survey information as well as historical data, machine learning models were used to map landslide susceptibility and help formulate landslide mitigation strategies. The advantages and limitations of several frequently employed algorithms were evaluated based on the accuracy and efficiency of landslide susceptibility forecasting models. As the result shows, the tree-based ensemble algorithms models achieved better compared with other commonly methods of papping landslide susceptibility. Furthermore, the effect of database quality and quantity is significant, and more applications of some advanced methods (i.e., deep learning algorithms) are yet to be further explored in further researches.