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Random forest tree for predicting fecal indicator organisms in drinking water supply.

, , and . BESC, page 1-6. IEEE, (2017)

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Machine learning: based detection of water contamination in water distribution systems., , and . GECCO (Companion), page 1664-1671. ACM, (2018)Quality Risk Analysis for Sustainable Smart Water Supply Using Data Perception., , , and . IEEE Trans. Sustain. Comput., 5 (3): 377-388 (2020)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)Prediction of Lane Clearance Time of Freeway Incidents Using the M5P Tree Algorithm., , and . IEEE Trans. Intell. Transp. Syst., 12 (4): 1549-1557 (2011)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)Robust night flow analysis in water distribution networks: A BiLSTM deep autoencoder approach., , and . Adv. Eng. Informatics, (October 2023)Random forest tree for predicting fecal indicator organisms in drinking water supply., , and . BESC, page 1-6. IEEE, (2017)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)Effect of Link-Level Variations of Connected Vehicles (CV) Proportions on the Accuracy and Reliability of Travel Time Estimation., , and . IEEE Trans. Intell. Transp. Syst., 20 (1): 87-96 (2019)