title: Estimating Escherichia coli loads in streams based on various physical, chemical, and biological factors.
abstract: Microbes have been identified as a major contaminant of water resources.Escherichia coli(E. coli) is a commonly used indicator organism. It is well recognized that the fate ofE. coliin surface water systems is governed by multiple physical, chemical, and biological factors. The aim of this work is to provide insight into the physical, chemical, and biological factors along with their interactions that are critical in the estimation ofE. coliloads in surface streams. There are various models to predictE. coliloads in streams, but they tend to be system or site specific or overly complex without enhancing our understanding of these factors. Hence, based on available data, a Bayesian Neural Network (BNN) is presented for estimatingE. coliloads based on physical, chemical, and biological factors in streams. The BNN has the dual advantage of overcoming the absence of quality data (with regards to consistency in data) and determination of mechanistic model parameters by employing a probabilistic framework. This study evaluates whether the BNN model can be an effective alternative tool to mechanistic models forE. coliloads estimation in streams. For this purpose, a comparison with a traditional model (LOADEST, USGS) is conducted. The models are compared for estimatedE. coliloads based on available water quality data in Plum Creek, Texas. All the model efficiency measures suggest that overallE. coliloads estimations by the BNN model are better than theE. coliloads estimations by the LOADEST model on all the three occasions (three-fold cross validation). Thirteen factors were used for estimatingE. coliloads with the exhaustive feature selection technique, which indicated that six of thirteen factors are important for estimatingE. coliloads. Physical factors included temperature and dissolved oxygen; chemical factors include phosphate and ammonia; biological factors include suspended solids and chlorophyll. The results highlight that the LOADEST model estimatesE. coliloads better in the smaller ranges, whereas the BNN model estimatesE. coliloads better in the higher ranges. Hence, the BNN model can be used to design targeted monitoring programs and implement regulatory standards through TMDL programs.
authors: Department of Biological and Agricultural Engineering, Texas A&M University, TX 77843.
authors: Dipankar
authors: D
authors: Dwivedi
authors: Department of Biological and Agricultural Engineering, Texas A&M University, TX 77843.
authors: Binayak P
authors: BP
authors: Mohanty
authors: Department of Biological and Agricultural Engineering, Texas A&M University, TX 77843.
authors: Bruce J
authors: BJ
authors: Lesikar
keywords: Bayesian Neural Networks; E. coli; LOADEST; Stream flow; Uncertainty analysis