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INTRODUCTION TO A COMBINED MULTIPLE LINEAR REGRESSION AND ARMA MODELING APPROACH FOR BEACH BACTERIA PREDICTION
Citation:
GE, Z. AND W. E. FRICK. INTRODUCTION TO A COMBINED MULTIPLE LINEAR REGRESSION AND ARMA MODELING APPROACH FOR BEACH BACTERIA PREDICTION. Presented at IAGLR's 50th Annual Conference on Great Lakes Research, University Park, PA, May 28 - June 01, 2007.
Impact/Purpose:
A main objective of this task is to combine empirical and physical mechanisms in a model, known as Visual Beach, that
- is user-friendly
- includes point and non-point sources of contamination
- includes the latest bacterial decay mechanisms
- incorporates real-time and web-based ambient and atmospheric and aquatic conditions
- and has a predictive capability of up to three days to help avert potential beach closures.
Description:
Due to the complexity of the processes contributing to beach bacteria concentrations, many researchers rely on statistical modeling, among which multiple linear regression (MLR) modeling is most widely used. Despite its ease of use and interpretation, there may be time dependences in the observations that cannot be explained by the MLR model, so that the residuals are not as random as they are assumed to be. In this case, an ARMA (auto-regressive moving average) model can be used to extract the possible deterministic time patterns from the MLR residuals. The ARMA-modeled deterministic part of the residual is then added to the MLR predictions as an adjustment, and the variance of the prediction errors can be considerably reduced. The whole modeling process is demonstrated with actual data from Huntington Beach, Ohio, in 2000-2004. Results show that the predictive capacity of the initial MLR model is significantly improved by making use of the supplemental ARMA technique. Supplemental ARMA modeling is an independent step that does not otherwise affect the existing MLR models, another attractive feature of this approach.