Studies using ML to assess the risks of OPs in drinking water remain limited. An early work mitigated the proliferation of Legionella in premise plumbing by controlling environmental variables . Using water flow and water temperature as the inputs, that study presented a NN-based simulator relying an approximate reasoning architecture (NARA) neurofuzzy system to predict and simulate water tank temperature. The simulator emulates conditions that inhibit the spread of Legionella in water networks. The NARA-based simulator achieves a high fidelity in mimicking water tank temperature with an accuracy exceeding 97%. A recent study integrated both unsupervised and supervised ML to correlate the spread of Legionella with environmental variables in retirement homes, health-related facilities, tourism-related buildings, and swimming-pools s from 2002 to 2019 in Italy . That study used an unsupervised ML algorithm to identify the spatiotemporal distribution of atypical Legionella