Lake Michigan Beaches are monitored during beach goer season every day by the Indiana Department of Environmental Management (IDEM). Based on E.Coli results with oneday lag, beach closures are made. During 2019, artificial neural network based E.Coli prediction tool was created to identify the E.Coli classes using 13 input parameters observed together with E.Coli. For this now-cast model, two classes were considered for the final model output (1 for safe level and 2 for E.Coli counts more than 235 colony forming units/100 ml). This year, IDEM recommended to update and fine-tune this model with additional data observed during 2020 through 2022. All the 13 inputs (including water temperature, pH, Total Dissolved solids, Total suspended solids, turbidity, trashes, bird counts, color, odor, Electrical conductivity) used in the 2019 model were collected from the beaches together with E.Coli data during 2020 to 2022 time period. This research paper presents the phase II results. After initial data consolidation, updated database had 761 data for the five beaches in the Northwest Indiana. After examining several architectures for the feedforward neural network model, the best model was identified. Cross validation of the model was also done by dividing the data into four blocks. New model results provided an overall predicting success of 93% for the two classes considered. However, the class 1 and class 2 prediction accuracies decreased from the original model developed using 2019 data. Class 1 and class 2 were predicted correctly 93% and 79% respectively. It resulted in a 10% decrease in class 2 predictions. A systematic examination of the different ANN model results in development stages and the influences of data variations in each year, were documented in this research work.
