Machine learning parallel system for integrated process-model calibration and accuracy enhancement in sewer-river system  

作　　者：Yundong Li Lina Ma Jingshui Huang Markus Disse Wei Zhan Lipin Li Tianqi Zhang Huihang Sun Yu Tian 

机构地区：[1]State Key Laboratory of Urban Water Resource and Environment(SKLUWRE),School of Environment,Harbin Institute of Technology,Harbin,150090,China [2]Chair of Hydrology and River Basin Management,Technical University Munich,Arcisstrasse 21,80333,Munich,Germany

出　　处：《Environmental Science and Ecotechnology》2024年第2期70-82,共13页环境科学与生态技术（英文）

基　　金：supported by the National Key R&D Program of China(2019YFD1100300);the Fellowship of China Postdoctoral Science Foundation(2020M681105);the State Key Laboratory of Urban Water Resource and Environment,Harbin Institute of Technology(No.2021TS23).

摘　　要：The process-based water system models have been transitioning from single-functional to integrated multi-objective and multi-functional since the worldwide digital upgrade of urban water system management.The proliferation of model complexity results in more significant uncertainty and computational requirements.However,conventional model calibration methods are insufficient in dealing with extensive computational time and limited monitoring samples.Here we introduce a novel machine learning system designed to expedite parameter optimization with limited data and boost efficiency in parameter search.MLPS,termed the machine learning parallel system for fast parameter search of integrated process-based models,aims to enhance both the performance and efficiency of the integrated model by ensuring its comprehensiveness,accuracy,and stability.MLPS was constructed upon the concept of model surrogation t algorithm optimization using Ant Colony Optimization(ACO)coupled with Long Short-Term Memory(LSTM).The optimization results of the Integrated sewer network and urban river model demonstrate that the average relative percentage difference of the predicted river pollutant concentrations increases from 1.1 to 6.0,and the average absolute percent bias decreases from 124.3%to 8.8%.The model outputs closely align with the monitoring data,and parameter calibration time is reduced by 89.94%.MLPS enables the efficient optimization of integrated process-based models,facilitating the application of highly precise complex models in environmental management.The design of MLPS also presents valuable insights for optimizing complex models in other fields.

关 键 词：Integrated sewereriver model LSTM ACO SewereWWTPeriver system Water pollution control strategy 

分 类 号：X52[环境科学与工程—环境工程]