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

ABSTRACT: 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

SUBMITTER: Li Y 

PROVIDER: S-EPMC10583054 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature