Water’s corrosion and scaling potential prediction using artificial neural networks and gene expression programming in several rural water distribution networks in Kermanshah Province, Iran

Document Type : Research Paper

Authors

1 Soil Science Department, Razi University

2 Department of Water Science and Engineering, Razi University

Abstract

Water quality causes severe restrictions on the utilization of water resources. Corrosion and scaling are the most common problems in the operation and maintenance of water facilities. Corrosive indices are an indirect method of detecting and measuring water's tendency to corrosion and scaling. Water corrosion and scaling are complex phenomena that cannot be easily modeled. This study used meta-heuristics methods such as artificial neural networks (ANN) and gene expression programming (GEP) to predict the water’s corrosion and scaling potential of the distribution network in some rural areas of Kermanshah province. Equations were extracted to estimate water corrosion and scaling indices using linear regression and GEP. The results showed that ANN can reveal water corrosion and scaling indices where correlation values were found to be 0.95, 0.91, 0.96, 0.92, and 0.99, and the lowest error percentages were 0.20, 0.44, 0.40, 0.44, and 0.08 for Langelier saturation index (LSI), Ryznar stability index (RSI), Puckorius scaling index (PSI), Aggressive index (AI), and Larson–Skold index (L-SI), respectively. Also, using linear and nonlinear relationships obtained by a high-precision GEP model (0.80 to 0.97), we can estimate corrosion and scaling indices with lower cost and more accuracy by measuring the most influential physicochemical parameters.

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Articles in Press, Accepted Manuscript
Available Online from 01 July 2025
  • Receive Date: 27 October 2024
  • Revise Date: 23 June 2025
  • Accept Date: 01 July 2025