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Collaborative Analysis for Computational Risk in Urban Water Supply Systems.

, , and . CIKM, page 2297-2300. ACM, (2019)

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Biodigester rapid analysis and design system (B-RADeS): A candidate attainable region-based simulator for the synthesis of biogas reactor structures., , , , , and . Comput. Chem. Eng., (2020)Machine learning: based detection of water contamination in water distribution systems., , and . GECCO (Companion), page 1664-1671. ACM, (2018)Smart Data Analysis for Water Quality in Catchment Area Monitoring., , , and . iThings/GreenCom/CPSCom/SmartData, page 900-908. IEEE, (2018)A Tensor Model for Quality Analysis in Industrial Drinking Water Supply System., , and . DASC/PiCom/DataCom/CyberSciTech, page 1090-1092. IEEE, (2019)Collaborative Analysis for Computational Risk in Urban Water Supply Systems., , and . CIKM, page 2297-2300. ACM, (2019)A Case-Based Reasoning Solution for Urban Drinking Water Quality Control., , , and . HPCC/DSS/SmartCity/DependSys, page 2454-2459. IEEE, (2021)Comparison of Adaptive Neuro-Fuzzy Inference System (ANFIS) and Gaussian Process for Machine Learning (GPML) Algorithms for the Prediction of Norovirus Concentration in Drinking Water Supply., , and . Trans. Large Scale Data Knowl. Centered Syst., (2017)Nodal vulnerability assessment of water distribution networks: An integrated Fuzzy AHP-TOPSIS approach., , , and . Int. J. Crit. Infrastructure Prot., (2021)A systematic framework for dynamic nodal vulnerability assessment of water distribution networks based on multilayer networks., , , and . Reliab. Eng. Syst. Saf., (2022)Toward A Sustainable Cyber-Physical System Architecture for Urban Water Supply System**This work was supported by KLIMAFORSK programme(No. 244147/E10) from Research Council of Norway., , and . iThings/GreenCom/CPSCom/SmartData/Cybermatics, page 482-489. IEEE, (2020)