摘要
针对普通神经网络模型确定滑坡易发性评价指标权重及易发性制图精度不高的问题,提出了一种新的权重确定方法和滑坡易发性评价模型。将BP神经网络模型和MIV理论相结合,获取最优隐藏节点数,优化神经网络模型。在此基础上,综合BP神经网络连接矩阵和MIV值确定滑坡易发性评价指标权重,构建滑坡评价模型。将评价模型应用于龙南县滑坡易发性制图,并利用ROC曲线对评价结果进行了检验。结果表明:MIV-BP模型具有较高的精度(AUC=0.820 4),在滑坡空间预测中具有更高的准确性和较大的应用潜力。
To solve the problem of low accuracy in the weight determination of landslide vulnerability evaluation indexes and the susceptibility mapping, a new weight determination method and landslide vulnerability evaluation model was proposed. BP neural network model and MIV method were combined to obtain the optimal number of hidden nodes and optimize the neural network model. On the basis, the weight of landslide vulnerability evaluation index was determined by synthesizing the BP neural network connection matrix and MIV value, and the landslide evaluation model was constructed. The model was applied to landslide susceptibility mapping in Longnan County, and performance of the proposed method was evaluated by ROC curve. The results indicated that MIV-BP model had high accuracy(AUC = 0.8204) and practicality in landslides spatial prediction.
作者
鲜木斯艳·阿布迪克依木
何书
ABUDIKEYINU XMSY;HE Shu(School Resources Environmental Engineering,Jiangxi University of Science and Technology,Ganzhou 341000,China;Jiangxi Key Laboratory of High Efficient Utilization of Rare Earth Resources,Jiangxi University of Science and Technology,Ganzhou 341000,China)
出处
《人民长江》
北大核心
2019年第11期140-144,共5页
Yangtze River
基金
江西省自然科学基金项目(20171BAB203029)
关键词
易发性分区
BP神经网络
MIV
空间预测
滑坡评价
susceptibility mapping
BP neural network
MIV
spatial prediction
landslide evaluation
