AI Article Synopsis
- "Random forests" are an advanced method used to explore the connections between response variables, such as bacteria density in water, and multiple explanatory variables.
- The study focused on five beaches and found that factors like the day of the week, previous day's bacteria density, water depth, and cloud cover influenced the bacteria levels.
- By analyzing initial data, researchers were able to predict future bacteria densities for up to 10 days, showcasing the potential effectiveness of this method for handling large and complex data sets.
Article Abstract
"Random forests," an extension of tree regression, provide a relatively new technique for exploring relationships of a response variable like the density of indicator bacteria in water to numerous potential explanatory variables. We used this tool to study relationships of indicator density at five beaches to numerous other variables and found that day of the week, indicator density 24h earlier, water depth at the sampling point, cloud cover, and others were related to density at one or more of the beaches. Using data from the first 52 days of measurement allowed predicting indicator densities in the following 10 days to order of magnitude at some of the beaches. Our analyses served to demonstrate the potential usefulness of this analytic tool for large data sets with many variables.
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| http://dx.doi.org/10.1016/j.watres.2005.01.001 | DOI Listing |
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