Real-Time Nowcasting of Microbiological Water Quality at Recreational Beaches: A Wavelet and Artificial Neural Network-Based Hybrid Modeling Approach

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 short-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 <i>Escherichia coli</i> 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 <i>R</i>² values between data and the NARX models (<i>R</i>² values of ∼0.8 for the beach sites and ∼0.9 for the river site) indicate that the new class of models shows promise for beach management

Comparative Evaluation of Statistical and Mechanistic Models of <i>Escherichia coli</i> at Beaches in Southern Lake Michigan

Statistical and mechanistic models are popular tools for predicting the levels of indicator bacteria at recreational beaches. Researchers tend to use one class of model or the other, and it is difficult to generalize statements about their relative performance due to differences in how the models are developed, tested, and used. We describe a cooperative modeling approach for freshwater beaches impacted by point sources in which insights derived from mechanistic modeling were used to further improve the statistical models and vice versa. The statistical models provided a basis for assessing the mechanistic models which were further improved using probability distributions to generate high-resolution time series data at the source, long-term “tracer” transport modeling based on observed electrical conductivity, better assimilation of meteorological data, and the use of unstructured-grids to better resolve nearshore features. This approach resulted in improved models of comparable performance for both classes including a parsimonious statistical model suitable for real-time predictions based on an easily measurable environmental variable (turbidity). The modeling approach outlined here can be used at other sites impacted by point sources and has the potential to improve water quality predictions resulting in more accurate estimates of beach closures

Environmental DNA (eDNA): A tool for quantifying the abundant but elusive round goby (<i>Neogobius melanostomus</i>) - Fig 5

<p>Captured still photo (GoPRO, Inc., San Mateo, California) of round goby at Portage Lakefront Breakwater, November 1, 2016 (A). Individuals highlighted with circles (B). Note extensive coverage by Dreissenid mussels. Depth = 1.5 m.</p

La Toque blanche : organe professionnel hebdomadaire des chefs de cuisine de France et de l'étranger

29 mai 19291929/05/29 (N186)

Conditions for mesocosm experiments.

<p>Conditions for mesocosm experiments.</p

Relationship between and number of round goby captured (natural log, LN) and concentration of round goby eDNA detected (natural log, LN), with best-fit regression line (Pearson R = 0.871).

<p>Relationship between and number of round goby captured (natural log, LN) and concentration of round goby eDNA detected (natural log, LN), with best-fit regression line (Pearson R = 0.871).</p

Map of the study areas in Lake Michigan and Lake Huron.

<p>Sampling sites in Lake Michigan (A) include the Portage Lakefront and Riverwalk breakwater (1), upstream sites (2 and 3), and a site above an elevation barrier (4). Sampling sites in Lake Huron (B) were located in Thunder Bay.</p