ArticleScientific reports2025
A machine learning approach for corrosion rate modeling in Patna water distribution network of Bihar.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Machine learning-based optimization of biogas and methane yields in UASB reactors for treating domestic wastewater.Biodegradation · 2025Article
- Integrated assessment of corrosion behaviour in drinking water distribution systems using experimental analysis, machine learning, and a unified corrosion-scaling index.Journal of environmental qualityArticle
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Authors and funding
6 authors.
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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, including multivariate adaptive regression splines (MARS), the group method of data handling (GMDH), and multivariate polynomial regression (MPMR), consider 13 features, including pH, temperature, conductivity, total dissolved solids, alkalinity, hardness, calcium hardness, magnesium hardness, chloride, sulfate, nitrate, dissolved oxygen, and time, as input parameters, with the corrosion rate as the output parameter. Energy dispersive X-ray (EDX) analysis revealed changes in composition before and after exposure: the carbon content decreased from 4 to 3%, the oxygen content increased from 20 to 31%, the iron content increased from 21 to 60%, the sulfur content decreased from 3 to 2%, the manganese content decreased from 3 to 1%, and the zinc content decreased from 49 to 1% by weight. The performance of the developed model was assessed via several performance metrics, regression error characteristic (REC) curves, comprehensive measurement (COM), and ranking techniques. On the basis of the performance of the developed models, the proposed MARS model is the most accurate model, with R
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