Abstract
Artificial Neural Networks are now widely applied in a broad range of fields, including image processing, signal processing, medical studies, financial predictions, power systems, and pattern recognition among others (Kosko, 1992; Refenes et al., 1994; Saund, 1989; Schalkoff, 1992; Suykens et. al. 1996; Vemuri and Rogers 1994). These successes have also inspired applications to water resources and environmental systems (Achela et al., 1998; Chang and Tsang, 1992; Derr and Slutz, 1994; French et al., 1992; Hsu et al, 1997; Hsu et al, 1995; Maier and Danday, 1996; Ranjithan and Eheart, 1993; Roger and Dowla, 1994). Because ANN models have the ability to recursively “learn from the data” they can result in significant savings in time required for model development, and are particularly useful for applications involving complicated, nonlinear processes that are not easily modelled by traditional means. This chapter addresses some issues related to the training of the class of ANNs known as Multi-layer Feedforward Neural Networks (MFNN) which are most commonly used in streamflow forecasting applications. We also present results illustrating the applicability of properly trained MFNNs in prediction of future streamflows from past rainfall and flows, and compare these results to those obtained by other modeling approaches.
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Gupta, H.V., Hsu, K., Sorooshian, S. (2000). Effective and Efficient Modeling for Streamflow Forecasting. 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_2
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DOI: https://doi.org/10.1007/978-94-015-9341-0_2
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