A machine learning approach for corrosion rate modeling in Patna water distribution network of Bihar.

ABSTRACT: Corrosion can affect water taste, color, and odor, making it crucial to monitor and control corrosion in the water distribution network to maintain water quality standards. This study used machine learning approaches such as MARS, GMDH, and MPMR to model the corrosion rate in water distribution networks. An experimental setup was established in the running water distribution network for data collection, where several test coupons were inserted into the pipeline. A coupon weight loss method was employed to calculate the corrosion rate. The selected study site was continuously monitored for 315 days to observe the water distribution network (WDN). The physicochemical parameters were regularly tested at the Environmental Engineering Laboratory at NIT Patna. Machine learning analyses, includin

SUBMITTER: Kumar S 

PROVIDER: S-EPMC11972417 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature