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基于小波分解与GA-LSSVM的GPS可降水量短临预报

针对GPS可降水量时间序列具有非线性、非平稳性的特征,研究一种基于小波分解（WD）、遗传算法（GA）和最小二乘支持向量机（LSSVM）的GPS可降水量短临预报方法。先采用小波分解将GPS可降水量时间序列分解成便于预报的低频分量和高频分量;然后利用遗传算法优化LSSVM参数,进而对各分量建立预报模型;再将各分量预报结果进行叠加重构得到最终预报结果。选取两组数据进行实验,并将预报结果分别与LSSVM和遗传小波神经网络（GA-WNN）预报结果进行对比。结果表明,该组合模型具有良好的泛化能力,可有效解决神经网络易陷于局部极小的问题,提高了全局预报精度。     

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地铁隧道地表变形小波去噪及灰色-时序组合预测模型研究

以青岛地铁3号线地表变形横向观测线实测数据为例,开展小波去噪及时序组合预测模型的研究。首先,采用小波理论对观测值进行粗差剔除与去噪处理,根据均方误差最低、信噪比最高的原则,证实dmey小波1层分解、rigrsure软阈值小波去噪方法是最优的。其次,给出地铁隧道地表变形灰色-时序组合预测模型表达式,选用等维新息GM(1,1)模型和残差时间序列模型进行地表变形叠合预测。最后,通过小波去噪后时间序列预测模型、小波去噪前灰色-时序组合预测模型、小波去噪后灰色-时序组合预测模型进行计算分析,结果表明小波去噪后灰色-时序组合模型预测精度最高,并分析了各模型预测精度差别的成因。     

Daily and Monthly Suspended Sediment Load Predictions Using Wavelet Based Artificial Intelligence Approaches

In the current study, the efficiency of Wavelet-based Least Square Support Vector Machine(WLSSVM) model was examined for prediction of daily and monthly Suspended Sediment Load(SSL) of the Mississippi River. For this purpose, in the first step, SSL was predicted via ad hoc LSSVM and Artificial Neural Network(ANN) models; then,streamflow and SSL data were decomposed into subsignals via wavelet, and these decomposed sub-time series were imposed to LSSVM and ANN to simulate discharge-SSL relationship. Finally, the ability of WLSSVM was compared with other models in multistep-ahead SSL predictions. The results showed that in daily SSL prediction, LSSVM has better outcomes with Determination Coefficient(DC)=0.92 than ad hoc ANN with DC=0.88. However unlike daily SSL, in monthly modeling, ANN has a bit accurate upshot.WLSSVM and wavelet-based ANN(WANN) models showed same consequences in daily and different in monthly SSL predictions, and adding wavelet led to more accuracy of LSSVM and ANN. Furthermore,conjunction of wavelet to LSSVM and ANN evaluated via multi-step-ahead SSL predictions and, e.g.,DC LSSVM=0.4 was increased to the DC WLSSVM=0.71 in 7-day ahead SSL prediction. In addition, WLSSVM outperformed WANN by increment of time horizon prediction.     

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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。      

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A WD-GA-LSSVM model for rainfall-triggered landslide displacement prediction

This paper proposes a WD-GA-LSSVM model for predicting the displacement of a deepseated landslide triggered by seasonal rainfall,in which wavelet denoising(WD)is used in displacement time series of landslide to eliminate the GPS observation noise in the original data,and genetic algorithm(GA)is applied to obtain optimal parameters of least squares support vector machines(LSSVM)model.The model is first trained and then evaluated by using data from a gentle dipping(~2°-5°)landslide triggered by seasonal rainfall in the southwest of China.Performance comparisons of WD-GA-LSSVM model with Back Propagation Neural Network(BPNN)model and LSSVM are presented,individually.The results indicate that the adoption of WD-GA-LSSVM model significantly improves the robustness and accuracy of the displacement prediction and it provides a powerful technique for predicting the displacement of a rainfall-triggered landslide.     

桥梁水平位移混沌特征识别与神经网络预测研究

跨海大桥系统受外界影响扰动,其变形伴有混沌现象发生.对桥梁变形监测数据实现了混沌识别,运用C-C法计算时间序列的延迟时间,用G-P方法求得最佳嵌入维数,通过求取的时间延迟和最佳嵌入维数对桥梁变形监测数据进行相空间重构,为混沌时间序列预测模型的建立奠定基础;基于RBF神经网络建立混沌时间序列预测模型,对实测数据进行桥梁变...