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| 基于PSOSVM算法的环境监测数据异常检测和缺失补全 |
| Anomaly Detection and Missing Completion of Environment Monitoring Data based on PSO SVM |
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| DOI： |
| 中文关键词: 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 |
| 英文关键词: Support vector machine (SVM) The particle swarm Environmental monitoring data Anomaly detection Missing completion Parameter optimization |
| 基金项目: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） |
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| 中文摘要: |
| 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。 |
| 英文摘要: |
| For problems of abnormal data and missing data in environmental monitoring, an anomaly detection and data missing completion algorithm was presented based on particle swarm optimization with support vector machine (PSO SVM). Non linear SVM model was established by applying the PSO algorithm in selecting the appropriate training parameter set and fitting prediction of real data. Taking the experimental data from a sewage plant 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. |
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