Estimating Escherichia coli loads in streams based on various physical, chemical, and biological factors

Key Points A BNN scheme is presented for estimating E. coli loads in streams E. coli load estimations by the BNN model are better than by the LOADEST model Physical, chemical, and biological factors are important for E‐coli load Microbes have been identified as a major contaminant of water resources. Escherichia coli is a commonly used indicator organism. It is well recognized that the fate of E. coli in 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 of E. coli loads in surface streams. There are various models to predict E. coli loads 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 estimating E. coli loads based on physical, chemical, and biological factors in streams. The BNN has the dual advantage of overcoming the absence of quality data (with regard 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 for E. coli load estimation in streams. For this purpose, a comparison with a traditional model (load estimator (LOADEST), U.S. Geological Survey) is conducted. The models are compared for estimated E. coli loads based on available water quality data in Plum Creek, Texas. All the model efficiency measures suggest that overall E. coli load estimations by the BNN model are better than the E. coli load estimations by the LOADEST model on all the three occasions (threefold cross validation). Thirteen factors were used for estimating E. coli loads with the exhaustive feature selection technique, which indicated that 6 of 13 factors are important for estimating E. coli loads. Physical factors included temperature and dissolved oxygen; chemical factors include phosphate and ammonia; and biological factors include suspended solids and chlorophyll. The results highlight that the LOADEST model estimates E. coli loads better in the smaller ranges, whereas the BNN model estimates E. coli loads better in the higher ranges. Hence, the BNN model can be used to design targeted monitoring programs and implement regulatory standards through total maximu