Online machine learning for stream wastewater influent flow rate prediction under unprecedented emergencies

作     者：Pengxiao Zhou Zhong Li Yimei Zhang Spencer Snowling Jacob Barclay Pengxiao Zhou;Zhong Li;Yimei Zhang;Spencer Snowling;Jacob Barclay

作者机构：Department of Civil EngineeringMcMaster UniversityHamiltonOntario L8S 4L8Canada MOE Key Laboratory of Resources and Environmental System OptimizationCollege of Environmental Science and EngineeringNorth China Electric Power UniversityBeijing 102206China Laboratory of Environmental Remediation and Functional MaterialSuzhou Research Academy of North China Electric Power UniversitySuzhou 215213China Hatch Ltd.Sheridan Science&Technology ParkMississaugaOntario L5K 2R7Canada 

出 版 物：《Frontiers of Environmental Science & Engineering》 (环境科学与工程前沿（英文）)

年 卷 期：2023年第17卷第12期

页      面：137-147页

核心收录：

学科分类：083002[工学-环境工程] 0830[工学-环境科学与工程（可授工学、理学、农学学位）] 08[工学] 

主　　题：Wastewater prediction Data stream Online learning Batch learning Influent flow rates 

摘      要：Accurate influent flow rate prediction is important for operators and managers at wastewater treatment plants(WWTPs),as it is closely related to wastewater characteristics such as biochemical oxygen demand(BOD),total suspend solids(TSS),and *** studies have been conducted to predict influent flow rate,and it was proved that data-driven models are effective ***,most of these studies have focused on batch learning,which is inadequate for wastewater prediction in the era of COVID-19 as the influent pattern changed *** learning,which has distinct advantages of dealing with stream data,large data set,and changing data pattern,has a potential to address this *** this study,the performance of conventional batch learning models Random Forest(RF),K-Nearest Neighbors(KNN),and Multi-Layer Perceptron(MLP),and their respective online learning models Adaptive Random Forest(aRF),Adaptive K-Nearest Neighbors(aKNN),and Adaptive Multi-Layer Perceptron(aMLP),were compared for predicting influent flow rate at two Canadian *** learning models achieved the highest R2,the lowest MAPE,and the lowest RMSE compared to conventional batch learning models in all *** R2 values on testing data set for 24-h ahead prediction of the aRF,aKNN,and aMLP at Plant A were 0.90,0.73,and 0.87,respectively;these values at Plant B were 0.75,0.78,and 0.56,*** proposed online learning models are effective in making reliable predictions under changing data patterns,and they are efficient in dealing with continuous and large influent data *** can be used to provide robust decision support for wastewater treatment and management in the changing era of COVID-19 and also under other unprecedented emergencies that could change influent patterns.