Journal
ENVIRONMENTAL SCIENCE & TECHNOLOGY
Volume 52, Issue 15, Pages 8446-8455Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acs.est.8b01022
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Funding
- National Natural Science Foundation of China [41530316]
- Fundamental Research Funds for the Central Universities of China [11618340]
- Cooperative Institute for Limnology and Ecosystems Research (CILER/NOAA) [NA120AR4320071]
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The number of beach closings caused by bacterial contamination has continued to rise in recent years, putting beachgoers at risk of exposure to contaminated water. Current approaches predict levels of indicator bacteria using regression models containing a number of explanatory variables. Data-based modeling approaches can supplement routine monitoring data and provide highly accurate shor-term forecasts of beach water quality. In this paper, we apply the nonlinear autoregressive network with exogenous inputs (NARX) method with explanatory variables to predict Escherichia coli concentrations at four Lake Michigan beach sites. We also apply the nonlinear input-output network (NIO) and nonlinear autoregressive neural network (NAR) methods in addition to a hybrid wavelet-NAR (WA-NAR) model and demonstrate their application. All models were tested using 3 months of observed data. Results revealed that the NARX models provided the best performance and that the WA-NAR model, which requires no explanatory variables, outperformed the NIO and NAR. models; therefore, the WA-NAR model is suitable for application to data scarce regions. The models proposed in this paper were evaluated using multiple performance metrics, including sensitivity and specificity measures, and produced results comparable or superior to those of previous mechanistic and statistical models developed for the same beach sites. The relatively high R-2 values between data and the NARX models (R-2 values of similar to 0.8 for the beach sites and similar to 0.9 for the river site) indicate that the new class of models shows promise for beach management.
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