摘要
针对环境监测数据异常和数据缺失问题,提出了基于支持向量机的粒子群优化数据异常检测和缺失补全算法。利用粒子群优化算法选取较优的支持向量机训练参数组合,以此建立非线性的支持向量机模型,并利用结果模型对测得的真实数据拟合预测。以宁夏回族自治区某污水处理厂的污染物测量数据作为实验数据,结果表明,利用该算法预测数据的准确率可达97.977%,检测异常数据准确度高,缺失数据补全正确。
For problems of abnormal data and missing data in environmental monitoring, an anomaly detectionand data missing completion algorithm was presented based on particle swarm optimization with support vectormachine （PSO-SVM ）. Non-linear SVM model was established by applying the PSO algorithm in selecting theappropriate training parameter set and fitting prediction of real data. Taking the experimental data from a sewageplant in Ningxia Hui Autonomous Region, the predictions by this algorithm had the accuracy rate of 97.977% ,showing high accuracy in abnormal data detection and missing data completion.
出处
《环境监测管理与技术》
CSCD
2016年第4期53-56,68,共5页
The Administration and Technique of Environmental Monitoring
基金
宁夏回族自治区环境保护厅科技攻关基金资助项目(2012005)
宁夏大学研究生创新基金资助项目(GTP201605)
关键词
支持向量机
粒子群
环境监测数据
异常检测
缺失补全
参数优化
Support vector machine （SVM ）
The particle swarm
Environmental monitoring data
Anomalydetection
Missing completion
Parameter optimization
