| 基于GIS与WOE-BP模型的滑坡易发性评价 |
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| 引用本文： | 郭子正,殷坤龙,付圣,黄发明,桂蕾,夏辉.基于GIS与WOE-BP模型的滑坡易发性评价[J].地球科学,2019,44(12):4299-4312. |
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| 作者姓名： | 郭子正 殷坤龙 付圣 黄发明 桂蕾 夏辉 |
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| 作者单位： | 1.中国地质大学工程学院, 湖北武汉 430074 |
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| 基金项目： | 国家重点研发计划项目2018YEC0809400国家自然科学基金项目41572292国家自然科学基金项目41907253 |
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| 摘 要： | 区域滑坡易发性研究对地质灾害风险管理具有重要意义.以往研究中，将多元统计模型与机器学习方法相结合用于滑坡易发性评价的研究较少.以三峡库区万州区为例，首先选取9种指标因子（坡度、坡向、剖面曲率、地表纹理、地层岩性、斜坡结构、地质构造、水系分布及土地利用类型）作为滑坡易发性评价指标.基于证据权模型（weights of evidence，WOE）计算得到的对比度和滑坡面积比与分级面积比的相对大小，对各指标因子进行状态分级；再利用粒子群法优化的BP神经网络模型（PSO-BP）得到各指标因子权重.综合两种模型确定的状态分级权重和指标因子权重（WOE-BP）计算滑坡易发性指数（landslide susceptibility index，LSI），基于GIS平台得到全区滑坡易发性分区图.结果表明:水系、地层岩性和地质构造是影响万州区滑坡发育的主要指标因子；WOE-BP模型的预测精度为80.8%，优于WOE模型的73.1%和BP神经网络模型的71.6%，可为定量计算指标因子权重和优化滑坡易发性评价提供有效途径.
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| 关 键 词： | 滑坡 指标因子 证据权模型 BP神经网络 GIS 地质灾害 |
| 收稿时间： | 2017-09-22 |
Evaluation of Landslide Susceptibility Based on GIS and WOE-BP Model |
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| Abstract: | Susceptibility assessment of region landslides plays an important role in geological hazard risk management. In previous studies, few of them applied the combination of multivariate statistic model and machine learning method to assess landslide susceptibility. Taking Wanzhou District of Three Gorges reservoir as an example, nine index factors including slope angle, slope direction, curvature, terrain surface texture, stratum lithology, slope structure, geological structure, water distribution and land use, were selected as the evaluation indexes of landslide susceptibility. The state of each index was graded based on the contrast values calculated by weights of evidence (WOE) model, landslide area ratio and grading area ratio firstly. Then the BP neural network model optimized by particle swarm optimization (PSO-BP) was applied to obtain the weight of each index. The landslide susceptibility index (LSI) was calculated by the combining weight of states and weight of indexes determined by these two models (WOE-BP) and landslide susceptibility mapping was obtained based on the GIS platform. The results indicate that water distribution, stratum lithology and geological structure are the main index factors influencing the development of landslides in Wanzhou District. The accuracy of the WOE-BP model reaches 80.8%, better than 73.1% of WOE model and 71.6% of BP neural network model. The proposed model provides an effective approach for calculating the weight of index quantificationally and optimizing the landslide susceptibility evaluation. |
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