Statistical assessment and neural network modeling of stream water quality observations of Green River watershed, KY, USA

• Published in: Water science & technology. Water supply 2019-09, Vol.19 (6), p.1831-1840
• Main Authors: Anmala, Jagadeesh, Venkateshwarlu, Turuganti
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Chemical oxygen demand; Cluster analysis; Creeks & streams; Dissolved oxygen; Environmental protection; Geographic information systems; Habitat loss; Hazard assessment; Health hazards; Hydrology; Mathematical models; Model accuracy; Modelling; Morphology; Neural networks; Nutrients; Organic matter; Parameters; Partial differential equations; Pollutants; Precipitation; Principal components analysis; Quality assessment; Regression analysis; Regression models; Riparian vegetation; Risk assessment; Rivers; Science; Statistical analysis; Statistical methods; Statistical models; Stream flow; Stream water; Surface water; Testing; Total dissolved solids; Training; Water bodies; Water pollution; Water quality; Water shortages; Water supply; Watershed management; Watersheds
• ISSN: 1606-9749; 1607-0798
• DOI: 10.2166/ws.2019.058

The measurement and statistical modeling of water quality data are essential to developing a region-based stream-wise database that would be of great use to the EPA's needs. Such a database would also be useful in bio-assessment and in the modeling of processes that are related to riparian vegetation surrounding a water body such as a stream network. With the help of easily measurable data, it would be easier to come up with database-intensive numerical and computer models that explain the stream water quality distribution and biological integrity and predict stream water quality patterns. Statistical assessments of nutrients, stream water metallic and non-metallic pollutants, organic matter, and biological species data are needed to accurately describe the pollutant effects, to quantify health hazards, and in the modeling of water quality and its risk assessment. The study details the results of statistical nonlinear regression and artificial neural network models for Upper Green River watershed, Kentucky, USA. The neural network models predicted the stream water quality parameters with more accuracy than the nonlinear regression models in both training and testing phases. For example, neural network models of pH, conductivity, salinity, total dissolved solids, and dissolved oxygen gave an R2 coefficient close to 1.0 in the testing phase, while the nonlinear regression models resulted in less than 0.6. For other parameters also, neural networks showed better generalization compared with nonlinear regression models.