Abstract
Neural networks are already used in watershed management to forecast, predict, and control water supplies, but to date have not been widely used to predict, control, or improve water quality. Yet, neural network based models can be applied to identify the presence of potentially hazardous, pathogen laden, agricultural runoff in receiving waters. This is an entirely novel approach for analysis of water quality indicators that, when expanded beyond source identification, may lead to development of new watershed monitoring and management paradigms.
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Brion, G.M., Lingireddy, S. (2000). Identification of Pollution Sources Via Neural Networks. 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_10
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DOI: https://doi.org/10.1007/978-94-015-9341-0_10
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