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Virtual Beach 3: User's Guide
Citation:
Cyterski, Mike, W. Brooks, M. Galvin, K. Wolfe, R. Carvin, T. Roddick, M. Fienen, AND S. Corsi. Virtual Beach 3: User's Guide. U.S. EPA Office of Research and Development, Washington, DC, EPA/600/R-13/311, 2013.
Impact/Purpose:
Virtual Beach Users Guide - Version 3
Description:
Virtual Beach version 3 (VB3) is a decision support tool that constructs site-specific statistical models to predict fecal indicator bacteria (FIB) concentrations at recreational beaches. VB3 is primarily designed for beach managers responsible for making decisions regarding beach closures or the issuance of swimming advisories due to pathogen contamination. However, researchers, scientists, engineers, and students interested in studying relationships between water quality indicators and ambient environmental conditions will find VB3 useful. VB3 reads input data from a text file or Excel document, assists the user in preparing the data for analysis, enables automated model selection using a wide array of possible model evaluation criteria, and provides predictions using a chosen model parameterized with new data. With an integrated mapping component to determine the geographic orientation of the beach, the software can automatically decompose wind/current/wave speed and magnitude information into along-shore and onshore/offshore components for use in subsequent analyses. Data can be examined using simple scatter plots to evaluate relationships between the response and independent variables (IVs). VB3 can produce interaction terms between the primary IVs, and it can also test an array of transformations to maximize the linearity of the relationship between the response variable and IVs. The software includes search routines for finding the "best" models from an array of possible choices. Automated censoring of statistical models with highly correlated IVs occurs during the selection process. Models can be constructed either using previously collected data or forecasted environmental information. VB3 has residual diagnostics for regression models, including automated outlier identification and removal using DFFITs or Cook's Distances.