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Application of Machine Learning Classification Algorithm to Precipitation-induced Landslides Forecasting
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摘要: 针对气象灾害预警业务中客观描述降雨型滑坡发生不确定性的实际需求，利用2014—2020年全国滑坡数据以及多源融合降水实况分析数据，通过样本构建、模型训练、参数优化以及预报输出等关键步骤构建基于机器学习分类算法的区域降雨诱发滑坡概率预报模型，探究不同类型机器学习分类算法识别诱发滑坡的降雨过程的可行性。结果表明：在算法评估中，线性判别分析算法准确率最高且泛化能力最好，其次为逻辑回归算法，再次为最邻近算法。在预报试验中，线性判别分析、逻辑回归以及最邻近等算法能够提取并学习降雨诱发滑坡的条件特征，对诱发滑坡的降雨过程有一定识别能力，最邻近算法和逻辑回归算法的概率预报高值区范围相对较大，易造成虚警结果，线性判别分析算法对局地降雨信息的提炼较好，但线性判别分析算法在非降雨中心区域输出低值概率预报的面积偏大。Abstract:
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.
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Key words:
- landslides;
- influence factors;
- machine learning;
- classification algorithms
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表 1 模型算法测试结果
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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表 2 降雨诱发滑坡个例
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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