Prediction of the disaster area of loess landslide based on least square support vector machine optimized by bat algorithm
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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。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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