• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.766-770
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• 2005

Cascade-Correlation Neural Networks Model(CCNNM) is used to estimate daily evaporation using limited climatical variables such as atmospheric temperature, dewpoint temperature, relative humidity, wind speed, sunshine duration and radiation. DeBruln equation is applied to estimate daily free-surface evaporation. It is converted into pan evaporation using pan coefficient. The results of CCNNM shows better than those of Debruin equation. This research represents that the strong nonlinear relationship such as evaporation modeling can be generalized by the CCNNM ; a special type of Backpropagation algorithm Neural Networks Model.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.1B
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• pp.47-62
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• 2009

The climate change caused by global warming may affect on the hydro-meteorologic factor such as evaporation (IPCC, 2001). Furthermore, it is also necessary that the effect of climate change according to geographical condition on evaporation should be studied. In this study, considering geographical and topographical conditions, the 6 evaporation equations that have been applied to simulate annual and monthly pan evaporation were compared. 56 climatologic stations were selected and classified, basing on the geographical and topographical characteristics (urbanization, topographical slope, proximity to coast, and area of water body). The evaporation equations currently being used are applied. These evaporation equations are Penman, Kohler-Nordenson-Fox (KNF), DeBruin-Keijman, Priestley-Taylor, Hargreaves, and Rohwer. Furthermore, Penman equation was modified by calibrating the parameters of wind function and was verified using relative error. The study results indicate that the KNF equation compared best with the pan: relative error was 8.72%. Penman equation provided the next-best values for evaporation relative to the pan: relative error was 8.75%. The mass-transfer method (Rohwer) provided the worst comparison showing relative error of 33.47%. In case that there is a close correlation between wind function and wind speed, modified Penman equation provided a better estimate of pan evaporation.