Dam safety prediction model considering chaotic characteristics in prototype monitoring data series
Dam safety prediction model considering chaotic characteristics in prototype monitoring data series
复制标题
考虑原型监测数据序列混沌特性的大坝安全预测模型
DOI：
10.1177/1475921716654963
复制
发表时间：
2016-11-01
影响因子：
6.6
通讯作者：
Tian, Shiguang
中科院分区：
文献类型：
--
作者：
Su, Huaizhi;Wen, Zhiping;Tian, Shiguang
将支持向量机、混沌理论和粒子群算法相结合，建立了大坝安全预测模型。提出了优化预测模型输入和参数的方法。首先，对坝体特性原型监测数据序列进行相空间重构。提出了一种识别监测数据序列混沌特性的方法。其次，采用支持向量机建立大坝安全预测模型。将历史监测数据作为支持向量机输入，通过相空间重构提取特征向量。引入混沌粒子群优化算法确定支持向量机参数。建立了基于混沌支持向量机的大坝安全预测模型。最后，以某实际大坝的位移特性为例。对所建立的大坝位移预测模型的预测能力进行了评价。结果表明，基于混沌支持向量机的模型能够提供更准确的预测结果，更适合用于有效地识别大坝的行为。
Support vector machine, chaos theory, and particle swarm optimization are combined to build the prediction model of dam safety. The approaches are proposed to optimize the input and parameter of prediction model. First, the phase space reconstruction of prototype monitoring data series on dam behavior is implemented. The method identifying chaotic characteristics in monitoring data series is presented. Second, support vector machine is adopted to build the prediction model of dam safety. The characteristic vector of historical monitoring data, which is taken as support vector machine input, is extracted by phase space reconstruction. The chaotic particle swarm optimization algorithm is introduced to determine support vector machine parameters. A chaotic support vector machine–based prediction model of dam safety is built. Finally, the displacement behavior of one actual dam is taken as an example. The prediction capability on the built prediction model of dam displacement is evaluated. It is indicated that the proposed chaotic support vector machine–based model can provide more accurate forecasted results and is more suitable to be used to identify efficiently the dam behavior.