Susceptibility assessment of debris flow in the upper reaches of the Minjiang River before and after the Wenchuan earthquake
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摘要:
科学准确地绘制泥石流易发性区划图以及确定主控因子及其贡献率，是区域泥石流预警预报和风险管理的重要基础。文章以岷江上游为研究区，以小流域为评价单元，分别采用了5种机器学习模型构建了泥石流易发性评价模型，对汶川大地震前、后岷江上游泥石流易发性和评价因子贡献率进行了定量分析。结果表明：（1）集成机器学习模型的预测精度及受试者工作特征曲线下面积值均高于浅层机器学习模型，其中随机森林模型在地震前、后泥石流易发性评价中表现最优；（2）震前、震后泥石流发生率均随易发性等级的提高逐渐增大，且等级越高增量越大，各等级震后泥石流发生率均高于震前；（3）地震前、后侵蚀传递系数的贡献率均显著高于其他因子，与汶川大地震地震烈度空间分布特征叠加，加大了震后干流和支流泥石流由下游向上游发育程度逐渐降低的空间分布规律。
Abstract:Accurately and scientifically mapping debris flow susceptibility and the determination of key controlling factors and their contribution rates are essential foundations for regional debris flow early warning, forecasting and risk management. The article takes the upper reaches of the Minjiang River as the research area, with small watersheds as evaluation units. Five different machine learning models were employed to construct evaluation models for the susceptibility of debris flows in the upper reaches of the Minjiang River. Quantitative analyses were conducted on the susceptibility of debris flows and the contribution rates of evaluation factors before and after the Wenchuan earthquake. The results indicate that: (1) Integrated machine learning models exhibit higher ACC and AUC values than the shallow machine learning models, with the random forest model performing the best in the assessment of debris flow susceptibility before and after the earthquake; (2) The occurrence rate of debris flow before and after the earthquakes gradually increases with the rise in susceptibility level, and the increment increases with the increase of the level. The occurrence rate of debris flow at all levels is higher after the earthquake than before; (3) The contribution rate of the erosion transmission coefficients before and after the earthquake is significantly higher than that of other factors. This contribution is compounded by the spatial distribution characteristics of the Wenchuan earthquake intensity, further accentuating the spatial distribution pattern of decreasing debris flow development from downstream to upstream in both the main and tributaries following the earthquake.
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表 2 测试集中各模型ACC及AUC值
Table 2. ACC and AUC values of the model on the test data set
NB RF XGB DT LR 震前 ACC 0.92 0.93 0.93 0.91 0.93 AUC 0.77 0.84 0.84 0.76 0.78 震后 ACC 0.83 0.86 0.85 0.81 0.85 AUC 0.84 0.91 0.90 0.83 0.90 
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