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Cost-effective sensor placement optimization for large-scale urban sewage surveillance

SUSTAINABLE CITIES AND SOCIETY 2024年 103卷

作者： Wang, Sunyu Xu, Ke Zhou, Yulun Univ Hong Kong Dept Urban Planning & Design Hong Kong Peoples R China Univ Calif Los Angeles Samueli Sch Engn Los Angeles CA 90095 USA Univ Hong Kong Musketeers Fdn Inst Data Sci Hong Kong Peoples R China

Early pandemic outbreak detection in cities is a crucial but challenging task. Complementary to the costly massive individual testing, urban sewage surveillance offers a rare, cost-effective solution for large-scale monitoring of pandemic spread in cities with minimal interference to people's lives. One emerging question is how to derive a cost-effective sensor placement plan in city -scale sewage networks having complicated topologies. Inspired by remote sensing, we first provide a general multi -objective formulation of the optimal sensor placement problem on directed networks. Then, we introduce a connectivity -based objective evaluation approach and embed it into an NSGA-II algorithm to enable efficient optimization on large-scale directed graphs. The effectiveness of the proposed method is verified on a real -world sewage network in Hong Kong serving more than 500,000 urban residents. Results show that the proposed method efficiently generated optimal sensor placement plans on city -scale networks. Optimized sensor placement plans outperformed human placement heuristics by a significant margin of 102%, highlighting the necessity for data -driven decision support for large-scale urban sensing. Methodologically, this study provides a benchmark problem and datasets for network -based spatial optimization studies. Codes and datasets developed in this study are open -sourced to support future research in a real -world scenario.

关键词： Optimal sensor placement network-based spatial optimization Multi-objective genetic algorithm Sewage surveillance Pandemic control

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