River flow prediction using artificial neural networks: generalisation beyond the calibration range.

By C. Imrie, S. Durucan, A. Korre. 2000

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Comparison of fuzzy inference systems for streamflow prediction (2009, background): “Their applications range from the forecasting of hourly and daily river stages or discharges (Thirumalaiah & Deo, 1998; Campolo et al., 1999, 2003; Imrie et al., 2000), groundwater modelling (Yang et al.”
Stochasticity, nonlinearity and forecasting of streamflow processes (2006, background): “Imrie et al. (2000) proposed the use of a guidance system to develop networks towards more general solutions.”
Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions (2010, background): “River systems in Bangladesh Level Imrie et al. (2000) Rivers Trent & Dove, UK Flow Anmala et al. (2000) Council Grove, El Dorado and Marion Rivers, USA Flow Kim and Barros (2001) Williamsburg, Raystown, Loyalsockville and Newport Rivers, USA Flow Khalil et al. (2001) English, Oslinka, Graham,…”
Using oceanic‐atmospheric oscillations for long lead time streamflow forecasting (2007, methodology): “ANNs have been used for a wide range of different learning-from-data applications and input-output correlations of nonlinear processes in water resources, and hydrology [Hsu et al., 1995; Zealand et al., 1999; Chang and Chen, 2001, Tigsanchali and Gautam, 2000; Imrie et al., 2000].”