A preliminary coupled MT–GA model for the prediction of highway runoff quality

• Published in: The Science of the total environment Vol. 407; no. 15; pp. 4490 - 4496
• Main Authors: Opher, Tamar, Friedler, Eran
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier B.V 15.07.2009; [Amsterdam; New York]: Elsevier Science; Elsevier
• Subjects: Algorithms; Applied sciences; Assessments; California; Chromium; Data-driven model (DDM); Earth sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Environmental Pollutants - analysis; Event mean concentration (EMC); Exact sciences and technology; Genetic algorithm (GA); Highway runoff; Highways; Mathematical models; Model tree (MT); Models, Theoretical; Natural water pollution; Pollutants; Pollution; Pollution, environment geology; Rainwaters, run off water and others; Runoff; Sensitivity analysis; Transportation; Water pollution; Water treatment and pollution; California; United States; North America; America; USA, California; Model tree (MT); Genetic algorithm (GA); Event mean concentration (EMC); Highway runoff; Data-driven model (DDM); Pollution prevention; Highway; Runoff water; Forecast model; Environmental management; Case study; Diffuse source; Algorithm performance; Genetic algorithm; Water quality; Water pollution
• ISSN: 0048-9697; 1879-1026
• DOI: 10.1016/j.scitotenv.2009.04.043

Pollutants accumulated on road pavement during dry periods are washed off the surface with runoff water during rainfall events, presenting a potentially hazardous non-point source of pollution. Estimation of pollutant loads in these runoff waters is required for developing mitigation and management strategies, yet the numerous factors involved and their complex interconnected influences make straightforward assessment impossible. Data-driven models (DDMs) have lately been used in water and environmental research and have shown very good prediction ability. The proposed methodology of a coupled MT–GA (model tree–genetic algorithm) model provides an effective, accurate and easily calibrated predictive model for EMC (event mean concentration) of highway runoff pollutants. The models were trained and verified using a comprehensive data set of runoff events monitored in various highways in California, USA. EMCs of Cr, Pb, Zn, TOC and TSS were modeled, using different combinations of explanatory variables. The models' prediction ability in terms of correlation between predicted and actual values of both training and verification data was mostly higher than previously reported values. Sensitivity analysis was performed to examine the relative significance of each explanatory variable and the models' response to changes in input values.