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Artificial Neural Networks in Hydrology pp 287–309Cite as

Application of Artificial Neural Networks to Forecasting of Surface Water Quality Variables: Issues, Applications and Challenges

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Part of the book series: Water Science and Technology Library ((WSTL,volume 36))

Abstract

Amidst growing concerns for the state of the world’s surface water resources, water quality modeling is assuming increasing importance. Thomann (1998) suggests that we are entering a’Golden Age’ of water quality modeling in which surface water models will make significant contributions towards fuller diagnosis of problems, uncovering surprises, providing a framework for decision-making and lessening future conflicts between environmental interests, managers and those who are regulated. Traditionally, there have been two main philosophical approaches to surface water quality modeling. Process based models consider the underlying physical processes directly, whereas statistical models determine relationships based on historical data sets. Of course, in reality, process based models usually require some degree of statistical calibration to historical data, whereas statistical models should be based, where possible, on relationships that have a physical basis. Recently, artificial neural networks (ANNs) have emerged as alternatives to traditional statistical models in a variety of fields, including water quality modeling.

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Authors and Affiliations

  1. Dept. of Civil and Environmental Engineering, University of Adelaide, Adelaide, South Australia, 5005

    H. R. Maier & G. C. Dandy

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  1. H. R. Maier
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  2. G. C. Dandy
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  1. Purdue University, West Lafayette, Indiana, USA

    R. S. Govindaraju  & A. Ramachandra Rao  & 

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Maier, H.R., Dandy, G.C. (2000). Application of Artificial Neural Networks to Forecasting of Surface Water Quality Variables: Issues, Applications and Challenges. In: Govindaraju, R.S., Rao, A.R. (eds) Artificial Neural Networks in Hydrology. Water Science and Technology Library, vol 36. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-9341-0_15

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  • DOI: https://doi.org/10.1007/978-94-015-9341-0_15

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