Development of a neural-based forecasting tool to classify recreational water quality using fecal indicator organisms

Srinivas Motamarri, Dominic L. Boccelli

Research output: Contribution to journalArticlepeer-review

37 Scopus citations


Users of recreational waters may be exposed to elevated pathogen levels through various point/non-point sources. Typical daily notifications rely on microbial analysis of indicator organisms (e.g., Escherichia coli) that require 18, or more, hours to provide an adequate response. Modeling approaches, such as multivariate linear regression (MLR) and artificial neural networks (ANN), have been utilized to provide quick predictions of microbial concentrations for classification purposes, but generally suffer from high false negative rates. This study introduces the use of learning vector quantization (LVQ) - a direct classification approach - for comparison with MLR and ANN approaches and integrates input selection for model development with respect to primary and secondary water quality standards within the Charles River Basin (Massachusetts, USA) using meteorologic, hydrologic, and microbial explanatory variables. Integrating input selection into model development showed that discharge variables were the most important explanatory variables while antecedent rainfall and time since previous events were also important. With respect to classification, all three models adequately represented the non-violated samples (>90%). The MLR approach had the highest false negative rates associated with classifying violated samples (41-62% vs 13-43% (ANN) and <16% (LVQ)) when using five or more explanatory variables. The ANN performance was more similar to LVQ when a larger number of explanatory variables were utilized, but the ANN performance degraded toward MLR performance as explanatory variables were removed. Overall, the use of LVQ as a direct classifier provided the best overall classification ability with respect to violated/non-violated samples for both standards.

Original languageEnglish (US)
Pages (from-to)4508-4520
Number of pages13
JournalWater research
Issue number14
StatePublished - Sep 15 2012
Externally publishedYes


  • Artificial neural network
  • Classification
  • Fecal coliform
  • Learning vector quantization
  • Linear regression
  • Surface water

ASJC Scopus subject areas

  • Ecological Modeling
  • Water Science and Technology
  • Waste Management and Disposal
  • Pollution


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