Monthly evaporation forecasting using artificial neural networks and support vector machines

Evaporation is one of the most important components of the hydrological cycle, but is relatively difficult to estimate, due to its complexity, as it can be influenced by numerous factors. Estimation of evaporation is important for the design of reservoirs, especially in arid and semi-arid areas. Art...

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Vydané v:	Theoretical and applied climatology Ročník 124; číslo 1-2; s. 69 - 80
Hlavní autori:	Tezel, Gulay, Buyukyildiz, Meral
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Vienna Springer Vienna 01.04.2016 Springer Nature B.V
Predmet:	Algorithms Aquatic Pollution Artificial intelligence Atmospheric Protection/Air Quality Control/Air Pollution Atmospheric Sciences Climate science Climatology Earth and Environmental Science Earth Sciences Evaporation Hydrologic cycle Hydrologic data Hydrologic modeling learning meteorology momentum Neural networks Original Paper Pan evaporation regression analysis Relative humidity semiarid zones support vector machines temperature Waste Water Technology Water Management Water Pollution Control Wind speed Artificial Neural Network Model Little Square Support Vector Machine Radial Basis Function Network Support Vector Machine Root Mean Square Error
ISSN:	0177-798X, 1434-4483
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