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Nodal vulnerability assessment of water distribution networks: An integrated Fuzzy AHP-TOPSIS approach.

, , , and . Int. J. Crit. Infrastructure Prot., (2021)

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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)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)A systematic framework for dynamic nodal vulnerability assessment of water distribution networks based on multilayer networks., , , and . Reliab. Eng. Syst. Saf., (2022)Nodal vulnerability assessment of water distribution networks: An integrated Fuzzy AHP-TOPSIS approach., , , and . Int. J. Crit. Infrastructure Prot., (2021)Detection of Water Safety Conditions in Distribution Systems Based on Artificial Neural Network and Support Vector Machine., , and . AISI, volume 845 of Advances in Intelligent Systems and Computing, page 567-576. Springer, (2018)Random forest tree for predicting fecal indicator organisms in drinking water supply., , and . BESC, page 1-6. IEEE, (2017)A parameter free Continuous Ant Colony Optimization Algorithm for the optimal design of storm sewer networks: Constrained and unconstrained approach.. Adv. Eng. Softw., 41 (2): 188-195 (2010)Energy efficient cooperative communication in single frequency networks., and . PIMRC, page 1719-1724. IEEE, (2010)Big Data Analytics for Wireless and Wired Network Design: A Survey., , , and . CoRR, (2018)