| 基于BA-LSSVM模型的黄土滑坡致灾范围预测 |
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| 引用本文： | 吴博, 赵法锁, 贺子光, 段钊, 吴韶艳. 2020. 基于BA-LSSVM模型的黄土滑坡致灾范围预测. 中国地质灾害与防治学报, 31(5): 1-6. doi: 10.16031/j.cnki.issn.1003-8035.2020.05.01 |
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| 作者姓名： | 吴博 赵法锁 贺子光 段钊 吴韶艳 |
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| 作者单位： | 1. 长安大学地质工程与测绘学院, 陕西 西安 710054;;; 2. 黄淮学院建筑工程学院, 河南 驻马店 463000;;; 3. 西安科技大学地质与环境学院, 陕西 西安 710054 |
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| 基金项目： | 国家自然科学基金资助项目:黄土边坡的地质结构界面效应及其促滑机制研究（41877247） |
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| 摘 要： | 滑坡致灾范围的预测研究一直是滑坡研究的重点难点之一。以陕西泾阳南塬滑坡为研究对象,选取滑坡高度、体积、滑源区长度以及宽度为影响因子,采用蝙蝠算法对最小二乘支持向量机中的正则化参数γ和σ2进行寻优计算,建立BA-LSSVM滑坡致灾范围预测模型,并于多元线性回归模型进行对比。结果表明,该模型具有较高的预测精度和效果,可作为该地区防灾减灾依据。
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| 关 键 词： | 蝙蝠算法 最小二乘支持向量机 黄土滑坡 致灾范围 预测 |
| 收稿时间： | 2020-04-02 |
| 修稿时间： | 2020-06-06 |
Prediction of the disaster area of loess landslide based on least square support vector machine optimized by bat algorithm |
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| WU Bo, ZHAO Fasuo, HE Ziguang, DUAN Zhao, WU Shaoyan. 2020. Prediction of the disaster area of loess landslide based on least square support vector machine optimized by bat algorithm. The Chinese Journal of Geological Hazard and Control, 31(5): 1-6. doi: 10.16031/j.cnki.issn.1003-8035.2020.05.01 |
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| Authors: | WU Bo ZHAO Fasuo HE Ziguang DUAN Zhao WU Shaoyan |
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| Affiliation: | 1. College of Geology Engineering and Geomatics, Chang'an University, Xi'an, Shaanxi 710054, China;;; 2. School of Architecture and Engineering, Huanghuai University, Zhumadian, Henan 463000, China;;; 3. College of Geology and Environment, Xi'an University of Science and Technology, Xi'an, Shaanxi 710054, China |
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| Abstract: | The prediction of landslide disaster area has always been one of the difficulties in landslide research. The loess landslides in South Jingyang plateau were chosen to establish model of disaster area prediction, by electing height, volume, source area length and width of landslide as the influencing factors, which based on the bat algorithm to optimize calculation for least squares support vector machine in the regularization parameters (γ and σ2). In the meantime, they are compared with mutiple linear regression model. The result shows that the model has better prediction accuracy and effect. It can be used as the basis for disaster prevention and reduction in the area. |
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| Keywords: | bat algorithm least squares support vector machine loess landslide disaster area prediction |
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