<p>A neural
network model was developed to predict the E. Coli levels and classes in six
(6) select Lake Michigan beaches. Water quality observations at the time of
sampling and discharge information from two close tributaries were used as
input to predict the E. coli. This research was funded by the Indiana Department
of Environmental Management (IDEM). A user-friendly Excel Sheet based tool was
developed based on the best model for making future predictions of E. coli
classes. This tool will facilitate beach managers to take real-time decisions.</p>
<p>The nowcast
model was developed based on historical tributary flows and water quality
measurements (physical, chemical and biological). The model uses experimentally
available information such as total dissolved solids, total suspended solids,
pH, electrical conductivity, and water temperature to estimate whether the E.
Coli counts would exceed the acceptable standard. For setting up this model,
field data collection was carried out during 2019 beachgoer’s season.</p>
<p>IDEM
recommends posting an advisory at the beach indicating swimming and wading are
not recommended when E. coli counts exceed advisory standards. Based on the
advisory limit, a single water sample shall not exceed an E. Coli count of 235 colony
forming units per 100 milliliters (cfu/100ml). Advisories are removed when
bacterial levels fall within the acceptable standard. However, the E. coli
results were available after a time lag leading to beach closures from previous
day results. Nowcast models allow beach managers to make real-time beach
advisory decisions instead of waiting a day or more for laboratory results to
become available.</p>
<p>Using the
historical data, an extensive experiment was carried out, to obtain the
suitable input variables and optimal neural network architecture. The best feed-forward
neural network model was developed using Bayesian Regularization Neural Network
(BRNN) training algorithm. Developed ANN model showed an average prediction
accuracy of around 87% in predicting the E. coli classes. </p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/12589247 |
| Date | 24 July 2020 |
| Creators | Mitra Khanibaseri (9045878) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Developing_Artificial_Neural_Networks_ANN_Models_for_Predicting_E_Coli_at_Lake_Michigan_Beaches/12589247 |
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