Using ANN to predict E. coli accumulation in coves based on interaction amongst various physical, chemical and biological factors

The accumulation of Escherichia Coli (E. coli) in canals, coves and streams is the result of a number of interacting processes operating at multiple spatial and temporal scales. Fate and transport of E. coli in surface water systems is governed by different physical, chemical, and biological processes. Various models developed to quantify each of these processes occurring at different scales are not so far pooled into a single predictive model. At present, very little is known about the fate and transport of E. coli in the environment. We hypothesize that E. coli population heterogeneity in canals and coves is affected by physical factors (average stream width and/ depth, secchi depth, flow and flow severity, day since precipitation, aquatic vegetation, solar radiation, dissolved and total suspended solids etc.); chemical factors (basic water quality, nutrients, organic compounds, pH, and toxicity etc.); and biological factors (type of bacterial strain, predation, and antagonism etc.). The specific objectives of this study are to: (1) examine the interactions between E. coli and various coupled physical, chemical and biological factors; (2) examine the interactions between E. coli and toxic organic pollutants and other pathogens (viruses); and (3) evaluate qualitatively the removal efficiency of E. coli. We suggest that artificial neural networks (ANN) may be used to provide a possible solution to this problem. To demonstrate the application of the approach, we develop an ANN representing E. coli accumulation in two polluted sites at Lake Granbury in the upper part of the Brazos River in North Central Texas. The graphical structure of ANN explicitly represents cause- and-effect relationship between system variables. Each of these relationships can then be quantified independently using an approach suitable for the type and scale of information available. Preliminary results revealed that E. coli concentrations in canals show seasonal variations regardless of change in sources of E. coli. This shows that E. coli accumulation depends on above listed external factors.