Abstract

Problem statement: The pan evaporation coefficient (Kp) is used to convert pan Evaporation (Ep) to reference Evapotranspiration (ETo) due to its simplicity and suitability for locations with limited availability of meteorological data. Approach: This study presents the use of neuro-genetic approach for estimating Kp for Class A pan and Colorado Sunken pan under green and dry fetch conditions. Results: Representative values were used to represent the category data, i.e., wind run and relative humidity. It was found that the genetic algorithm helped automatically search for the optimal structure of the back-propagation network, replacing the very tedious trial and error approach. Conclusion: A comparative analysis showed that the neural-genetic approach fairly outperformed previous proposed Kp equations for both green and dry fetch conditions. © 2011 Science Publications.