库岸滑坡地下水位时间序列混沌特征识别与PSO-LSSVM模型预测

地下水位预测对滑坡稳定性分析具有重要意义,三峡库区库岸滑坡地下水位时间序列在季节性强降雨和周期性库水位涨落等诸多因素影响下呈现混沌特征。在对地下水位序列进行相空间重构的基础上,采用饱和关联维数法和最大Lyapunov指数法对其混沌特征进行验证。再用预测性能优秀的最小二乘支持向量机(LSSVM)模型对其进行预测,并用粒子群算法优化选取LSSVM模型的参数,以克服LSSVM模型参数选取困难的缺点。以三峡库区三舟溪滑坡前缘STK-1水文孔日平均地下水位序列为例进行了混沌分析,分别运用粒子群优化的LSSVM模型(PSO-LSSVM)和BP神经网络模型对STK-1水文孔地下水位进行了预测。结果表明库岸滑坡地下水位序列存在混沌特征,PSO-LSSVM模型预测结果的均方根误差为0.193m,拟合优度为0.815,说明预测效果较理想,且PSO-LSSVM模型预测精度高于BP网络模型,具有较强的实用性。 

Chaotic Characteristics Identification and Prediction Using PSO-LSSVM Model of Reservoir Landslide Groundwater Level Time Series

Reservoir landslide groundwater level time series prediction in Three Gorges Reservoir area is of great significance for landslide stability analysis.The groundwater level time series may be of chaotic characteristics under the impact of external factors such as seasonal heavy rainfall and periodic reservoir water level fluctuation.Based on the phase space reconstruction of reservoir landslide groundwater level time series,saturation correlation dimension method and maximum Lyapunov exponent method were used to verify the existence of chaotic characteristics of groundwater level time series.Then Least Squares Support Vector Machine(LSSVM)model with high prediction accuracy was proposed for groundwater level time series prediction.The Particle Swarm Optimization(PSO)was applied to select the optimal combination for the parameters of LSSVM model.The proposed PSO-LSSVM model can resolve the difficulty in parameters selection of LSSVM model.Daily average groundwater level series of STK-1hydrology hole on the Sanzhouxi landslide in Three Gorges Reservoir area was taken as an example to verify the existence of chaotic characteristics.PSO-LSSVM model was compared with BP Neural Network model.The results show that the groundwater level series is of obvious chaotic characteristics,the Root-Mean-Square Error and goodness of fit of PSO-LSSVM model are 0.193 mand 0.815,respectively.In addition,prediction accuracy of the proposed model is higher than that of BP Neural Network model.The proposed model reflects the evolution law of reservoir landslide groundwater level time series effectively and thus is greatly practicable.