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

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

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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 man-agement.The proliferation of model complexity results in more significant uncertainty and computa-tional 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 in-tegrated 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+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.

Integrated sewer-river modelLSTMACOSewer-WWTP-river systemWater pollution control strategy

Mohamed Mannaa、Abdelaziz Mansour、Inmyoung Park、Dae-Weon Lee、Young-Su Seo、Lipin Li、Tianqi Zhang、Huihang Sun、Yu Tian

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Department of Integrated Biological Science,Pusan National University,Busan,46241,Republic of Korea

Department of Plant Pathology,Cairo University,Faculty of Agriculture,Giza,12613,Egypt

Department of Economic Entomology and Pesticides,Faculty of Agriculture,Cairo University,Giza,12613,Egypt

School of Food and Culinary Arts,Youngsan University,Bansong Beltway,Busan,48015,Republic of Korea

Department of SmartBio,Kyungsung University,Busan,48434,Republic of Korea

State Key Laboratory of Urban Water Resource and Environment(SKLUWRE),School of Environment,Harbin Institute of Technology,Harbin,150090,China

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国家重点研发计划Fellowship of China Postdoctoral Science FoundationState Key Laboratory of Urban Water Resource and Environment,Harbin Institute of Technology

2019YFD11003002020M6811052021TS23

2024

10.1016/j.ese.2023.100320

环境科学与生态技术(英文)

环境科学与生态技术(英文)

ISSN：
年,卷(期)：2024.18(1)
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