ISSN 1003-8035 CN 11-2852/P
    彭双庆,刘朋飞,陈刚,等. 基于IV-RF耦合模型和临界月平均降雨阈值的区域滑坡危险性评价研究−以重庆市涪陵区为例[J]. 中国地质灾害与防治学报,2024,35(0): 1-14. DOI: 10.16031/j.cnki.issn.1003-8035.202402015
    引用本文: 彭双庆,刘朋飞,陈刚,等. 基于IV-RF耦合模型和临界月平均降雨阈值的区域滑坡危险性评价研究−以重庆市涪陵区为例[J]. 中国地质灾害与防治学报,2024,35(0): 1-14. DOI: 10.16031/j.cnki.issn.1003-8035.202402015
    PENG Shuangqing,LIU Pengfei,CHEN Gang,et al. Regional Landslide Hazard Assessment using the IV-RF Coupling Model and Critical Monthly Average Rainfall Threshold:A case study from Fuling District,Chongqing[J]. The Chinese Journal of Geological Hazard and Control,2024,35(0): 1-14. DOI: 10.16031/j.cnki.issn.1003-8035.202402015
    Citation: PENG Shuangqing,LIU Pengfei,CHEN Gang,et al. Regional Landslide Hazard Assessment using the IV-RF Coupling Model and Critical Monthly Average Rainfall Threshold:A case study from Fuling District,Chongqing[J]. The Chinese Journal of Geological Hazard and Control,2024,35(0): 1-14. DOI: 10.16031/j.cnki.issn.1003-8035.202402015

    基于IV-RF耦合模型和临界月平均降雨阈值的区域滑坡危险性评价研究以重庆市涪陵区为例

    Regional Landslide Hazard Assessment using the IV-RF Coupling Model and Critical Monthly Average Rainfall Threshold:A case study from Fuling District,Chongqing

    • 摘要: 提高降雨型滑坡易发性预测精度和构建适合的降雨阈值模型对区域滑坡危险性评价具有重要意义。以重庆市涪陵区为例,采用信息量模型、BP神经网络模型、随机森林模型、信息量-BP神经网络耦合模型和信息量-随机森林耦合模型进行区域滑坡易发性评价,对比不同模型下的接受者操作特征曲线、曲线下方面积和易发性分布规律。提出滑坡临界月平均降雨阈值模型,反演出不同时间概率下的临界月平均降雨阈值。将易发性结果与时间概率等级进行耦合得到区域滑坡危险性评价结果并随机选取30次滑坡事件与4次典型滑坡事件进一步验证了评价精度。研究结果表明:信息量和机器学习模型进行耦合,弥补了机器学习在前期数据输入和非样本选择的缺点,提升了单一机器学习模型的预测精度,其中信息量-随机森林耦合模型预测精度最高;随机选取的30例滑坡样本中,有20例滑坡(占67%)位于发生时间概率50%以上区域,验证了临界月平均降雨阈值模型的精度;随机选取的4例典型滑坡样本中,时间概率等级基本为P4或P5,且位置均位于高危险区与极高危险区中,与现场调查结果基本一致,说明基于信息量-随机森林耦合模型和临界月平均降雨阈值的区域滑坡危险性评价结果准确且可靠。

       

      Abstract: Improving the accuracy of susceptibility prediction for rainfall-induced landslides and establishing suitable rainfall threshold models are of great significance for regional landslide hazard assessment. Taking Fuling District of Chongqing as a case study, the Information Value Model, BP Neural Network Model, Random Forest Model, Information Value-BP Neural Network Coupled Model, and Information Value-Random Forest Coupled Model were used to evaluate regional landslide susceptibility. By comparing the Receiver Operating Characteristic (ROC) curves, Area Under the Curve (AUC), and susceptibility distribution patterns of different models, a critical monthly average rainfall threshold model for landslides is proposed, and critical monthly average rainfall thresholds for different temporal probabilities were inferred. The susceptibility results were coupled with temporal probability levels to produce regional landslide hazard assessment results. The evaluation accuracy is further validated with 30 randomly selected landslide events and 4 typical landslide cases. The results show that coupling the Information Value and machine learning models compensates for the shortcomings of machine learning in early data input and non-sample selection, enhancing the predictive accuracy of single machine learning models. Among these, the Information Value-Random Forest Coupled Model exhibits the highest predictive accuracy; of the 30 randomly selected landslide samples, 20 cases (67%) occurred in areas with a temporal probability of over 50%, validating the accuracy of the critical monthly average rainfall threshold model. The 4 typical landslide samples selected randomly were primarily in the P4 or P5 temporal probability levels and were located in high to very high-risk areas, aligning well with field survey results. This indicates that the regional landslide hazard assessment based on the Information Value-Random Forest Coupled Model and the critical monthly average rainfall threshold is accurate and reliable.

       

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