摘要
滑坡易发性评价是滑坡灾害管理的基础工作,也是制定各项防灾减灾措施的重要依据。针对传统的信息量模型在评价过程中确定权重值存在准确性不高的缺点,文章提出RBF神经网络和信息量耦合模型。以甘肃省岷县为研究区,筛选坡度等9个指标因子构建了滑坡灾害易发性评价指标体系,应用RBF神经网络-信息量耦合模型(RBFNN-I)进行滑坡灾害易发性评价,利用合理性检验和ROC曲线对模型的评价结果进行精度检验。结果表明:(1)RBFNN-I模型的AUC值为0.853,相比单一的RBFNN和I模型分别提高了6.3%和9.7%,说明RBFNN-I模型具有更好的评价精度;(2)岷县滑坡灾害的极高易发区和高易发区主要分布在临潭—宕昌断裂带、洮河及其支流、闾井河和蒲麻河两侧河谷地带,距断层距离、降雨量、距道路距离和NDVI是影响岷县滑坡灾害分布的主控因子。
The landslide susceptibility evaluation is the basic work of landslide management,and it is also an important basis for formulating various disaster prevention and mitigation measures.In view of the low accuracy of the traditional information model in determining the weight value in the evaluation process,this paper proposes a coupling model of RBF neural network and Information value model.9 index factors such as slope are selected to build the evaluation index system of landslide susceptibility in Min Xian of Gansu Province.The RBF neural network information value coupling model(RBFNN-I)is used to carry out the landslide hazard susceptibility evaluation.Rationality test and ROC curve are used to test the accuracy of the evaluation results of the model.The results show that:(1)the AUC value of RBFNN-I model is 0.853,which is 6.3%and 9.7%higher than that of single RBFNN and I model,respectively,indicating that RBFNN-I model has better evaluation accuracy;(2)the extremely high and high susceptible areas of landslide disasters in Min Xian are mainly distributed along Lintan-Dangchang fault zone,Tao He and its tributaries,and the valleys on both sides of Lyuning River and Puma River.
作者
黄立鑫
郝君明
李旺平
周兆叶
贾佩钱
HUANG Lixin;HAO Junming;LI Wangping;ZHOU Zhaoye;JIA Peiqian(College of Civil Engineering,Lanzhou University of Technology,Lanzhou,Gansu 730050,China;Emergency Mapping Engineering Research Center of Gansu,Lanzhou,Gansu 730050,China)
出处
《中国地质灾害与防治学报》
CSCD
2021年第6期116-126,共11页
The Chinese Journal of Geological Hazard and Control
基金
甘肃省高等学校产业支撑计划项目“地质灾害隐患识别、临灾预警与防治关键技术研究”(2020C-40)
甘肃省自然科学基金“附加监测数据的滑坡稳定性评价”(20JR10RA180)
甘肃省自然科学基金“黄河源区高寒灌丛变化及其影响机制研究”(20JR5RA444)。
关键词
滑坡
易发性评价
RBF神经网络
信息量
岷县
landslide
susceptibility assessment
RBF neural network
information value
Min Xian