| 模型算法 | ACC | AUC |
| 线性判别分析 | 0.863 | 0.886 |
| 最邻近 | 0.838 | 0.858 |
| 逻辑回归 | 0.840 | 0.879 |
| 随机森林 | 0.834 | 0.849 |
| 支持向量机 | 0.821 | 0.819 |
| 决策树 | 0.832 | 0.841 |
| 临界阈值 | 0.658 | 0.693 |
| Citation: | Liu Haizhi, Xu Hui, Bao Hongjun, et al. Application of machine learning classification algorithm to precipitation-induced landslides forecasting. J Appl Meteor Sci, 2022, 33(3): 282-292. DOI: 10.11898/1001-7313.20220303.
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To address the practical needs of objectively describing the uncertainty of rainfall-based landslides and the existing problems of single warning indicators and subjective forecasting methods in the meteorological disaster early warning business, landslide disaster data from 2014 to 2020 and multi-source used precipitation analysis data are investigated to construct a regional rainfall-induced landslides probability forecasting model. Machine learning classification algorithms is implemented through key steps such as sample construction, model training, parameter optimization and forecast output to explore the feasibility of different types of algorithms in identifying landslides-causing rainfall processes. A training sample set construction method based on the positive samples, the negative samples are obtained by sampling under spatial-temporal limitation. The evaluation of different machine learning classification algorithms using the sample set shows that linear discriminant analysis algorithm has the highest accuracy(0.863) and the best generalization ability(area under the receiver operating characteristic curve is 0.886) without over-fitting problem, followed by the logistic regression algorithm and the K-nearest neighbor algorithm. In the probabilistic forecasting test for the cases of rainfall-induced landslides in 2021, all of three algorithms can extract and learn the conditional features and have certain ability to identify the rainfall processes which induce landslides. K-nearest neighbor algorithms and logistic regression algorithms have a relatively large range of probabilistic forecasting high value areas, which are prone to false alarm results. The probability forecast of the linear discriminant analysis algorithms is more convergent in the range of the high value area, and it can extract local rainfall information better, but it outputs unnecessary low-value probability forecasts in non-rainfall central area. The rainfall-induced landslides probability prediction model based on the machine learning classification algorithm comprehensively considers the coupling effect of the underlying surface factor and the rainfall factor, which is better than the commonly used critical threshold model that assumes the occurrence of landslides in the forecast area is only related to rainfall. The application results show that the machine learning classification algorithm model makes up for the shortcomings of existing forecasting models that are less likely to reflect the influence of the surface environment, so it is an important way to improve the performance of landslides forecasting and warning.
Fig. 1 Distribution of landslides in 2014-2020
Fig. 2 Underlying surface type
Fig. 3 Landslides susceptibility
Fig. 4 Rainfall critical threshold model
Fig. 5 Probability forecast results at 2000 BT 21 May 2021
Fig. 6 Probability forecast results at 2000 BT 4 Oct 2021
Table 1 Model algorithms test results
| 模型算法 | ACC | AUC |
| 线性判别分析 | 0.863 | 0.886 |
| 最邻近 | 0.838 | 0.858 |
| 逻辑回归 | 0.840 | 0.879 |
| 随机森林 | 0.834 | 0.849 |
| 支持向量机 | 0.821 | 0.819 |
| 决策树 | 0.832 | 0.841 |
| 临界阈值 | 0.658 | 0.693 |
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Table 2 Cases of rainfall-induced landslides
| 编号 | 发生时间 | 发生位置 | 阈值模型预报 |
| 个例1 | 2021-05-22T04:00 | 福建省宁德市国道104线福安路段(27.0°N，119.7°E) | 未发生滑坡 |
| 个例2 | 2021-07-26T10:00 | 浙江省绍兴市柯桥区平水镇下灶村(29.9°N，120.7°E) | 发生滑坡 |
| 个例3 | 2021-08-29T14:00 | 重庆市开州区关面乡关面社区(31.6°N，108.9°E) | 发生滑坡 |
| 个例4 | 2021-09-05T16:00 | 四川省巴中市通江县空山镇五福村(32.5°N，107.4°E) | 发生滑坡 |
| 个例5 | 2021-10-05T23:00 | 山西省临汾市蒲县蒲城镇荆坡村(36.4°N，111.1°E) | 未发生滑坡 |
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