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

Microbes have been identified as a major contaminant of water resources. () is a commonly used indicator organism. It is well recognized that the fate of 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 loads in surface streams. There are various models to predict 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 loads 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 for loads estimation in streams. For this purpose, a comparison with a traditional model (LOADEST, USGS) is conducted. The models are compared for estimated loads based on available water quality data in Plum Creek, Texas. All the model efficiency measures suggest that overall loads estimations by the BNN model are better than the loads estimations by the LOADEST model on all the three occasions (three-fold cross validation). Thirteen factors were used for estimating loads with the exhaustive feature selection technique, which indicated that six of thirteen factors are important for estimating loads. 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 estimates loads better in the smaller ranges, whereas the BNN model estimates loads better in the higher ranges. Hence, the BNN model can be used to design targeted monitoring programs and implement regulatory standards through TMDL programs.