Predictive modeling for wastewater applications: Linear and nonlinear approaches

  • Authors:

  • Scott A. Dellana;David West

  • Affiliations:

  • 3412 Harold Bate Building, College of Business, East Carolina University, Greenville, NC 27858, USA;3205 Harold Bate Building, College of Business, East Carolina University, Greenville, NC 27858, USA

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2009

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Abstract

This study compares the multi-period predictive ability of linear ARIMA models to nonlinear time delay neural network models in water quality applications. Comparisons are made for a variety of artificially generated nonlinear ARIMA data sets that simulate the characteristics of wastewater process variables and watershed variables, as well as two real-world wastewater data sets. While the time delay neural network model was more accurate for the two real-world wastewater data sets, the neural networks were not always more accurate than linear ARIMA for the artificial nonlinear data sets. In some cases of the artificial nonlinear data, where multi-period predictions are made, the linear ARIMA model provides a more accurate result than the time delay neural network. This study suggests that researchers and practitioners should carefully consider the nature and intended use of water quality data if choosing between neural networks and other statistical methods for wastewater process control or watershed environmental quality management.