Due to sensor malfunctions and communication faults,multiple missing patterns frequently happen in wastewater treatment process(WWTP).Nevertheless,the existing missing data imputation works cannot stand multiple missi...
详细信息
Due to sensor malfunctions and communication faults,multiple missing patterns frequently happen in wastewater treatment process(WWTP).Nevertheless,the existing missing data imputation works cannot stand multiple missing patterns because they have not sufficiently utilized of data *** this article,a double-cycle weighted imputation(DCWI)method is proposed to deal with multiple missing patterns by maximizing the utilization of the available information in variables and *** proposed DCWI is comprised of two components:a double-cycle-based imputation sorting and a weighted K nearest neighbor-based imputation ***,the double-cycle mechanism,associated with missing variable sorting and missing instance sorting,is applied to direct the missing values ***,the weighted K nearest neighbor-based imputation estimator is used to acquire the global similar instances and capture the volatility in the local *** estimator preserves the original data characteristics as much as possible and enhances the imputation ***,experimental results on simulated and real WWTP datasets with non-stationarity and nonlinearity demonstrate that the proposed DCWI produces more accurate imputation results than comparison methods under different missingpatterns and missing ratios.
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