11/14/2017 Paper accepted for publication in ASCE-Journal of Environmental Engineering

Our paper "Modeling Fecal Indicator Bacteria in Urban Waterways using Artificial Neural Networks" has been accepted for publication in ASCE-Journal of Environmental Engineering.

Abstract: Fecal indicator bacteria (FIB) are used as proxies to measure microbial water quality of aquatic ecosystems. Methods of modeling FIB have evolved in order to provide accurate and timely prediction to inform decisions by governing authorities to prevent risk to public health. In this study, a predictive model to forecast the FIB concentrations of an urban waterway, the Chicago River, utilizing the artificial neural network (ANN) method is developed. To address tuning of hyperparameters of the ANN model, an exhaustive testing is performed to select optimal hyperparameters. RMSprop optimizer performs better than SGD and Adam optimizers in this study. Eight input variables are eventually selected from ten initially proposed variables: water temperature, turbidity, daily, 2-day, and 7-day cumulative rainfall, river flow discharge, distance from the upstream water reclamation plant, and number of upstream combined sewer outfalls. Water reclamation plants and combined sewer overflows are found to be critical contributors of microbial pollution in this urban waterway and should be considered in the ANN model. The developed model has an accuracy of 86.5% to predict whether fecal coliform concentration is above or below a regulatory threshold.