摘要
针对城市污水处理运行过程中出现混合异常数据的问题,提出了一种基于改进型随机森林的数据清洗方法.首先,设计了一个孤立森林的异常数据识别模型,识别数据中的离群值.其次,建立了一种改进型随机森林回归模型,提高随机森林对混合类型异常数据的适应能力,并对数据趋势进行拟合预测.最后,用改进的随机森林数据清洗方法对剔除混合异常数据后的缺失数据进行补偿,实现对污水数据的清洗.实际数据测试结果表明,该方法提高了混合类型缺失数据补偿的准确性.
To reduce the impact of different types of abnormal data in the municipal wastewater treatment processes,a data cleaning method was proposed in this paper based on improved random forest.First,an anomaly detection model for isolated forest was designed to detect the outlier data.Second,an improved random forest regression model was used to predict the missing data,which improved the random forest to adapt to the mixed type missing data.Third,the detected abnormal data was eliminated.Finally,the improved random forest was used to predict and compensate the missing data of mixed types.This cleaning method was tested through the municipal wastewater treatment data.Results show that the method improves the accuracy of compensation for mixed type missing data.
作者
韩红桂
赵子凡
伍小龙
杨士恒
何政
赵楠
HAN Honggui;ZHAO Zifan;WU Xiaolong;YANG Shiheng;HE Zheng;ZHAO Nan(Faculty of Information Technology,Beijing University of Technology,Beijing 100124,China;Beijing Key Laboratory of Computational Intelligence and Intelligent System,Beijing 100124,China;Beijing City Drainage Refco Group Ltd.,Beijing 100044,China)
出处
《北京工业大学学报》
CAS
CSCD
北大核心
2021年第5期421-430,共10页
Journal of Beijing University of Technology
基金
北京高校卓越青年科学家资助项目(BJJWZYJH01201910005020)
国家重点研发计划资助项目(2018YFC1900800-5)
国家自然科学基金重大资助项目(61890930-5)。
关键词
污水处理
异常数据
数据清洗
孤立森林
随机森林
数据补偿
wastewater treatment
abnormal data
data cleaning
isolated forest
random forests
data compensation
作者简介
韩红桂(1983-),男,教授,主要从事智能信息处理及污水处理过程智能优化控制方面的研究,E-mail:rechardhan@bjut.edu.cn。
