Estimation of pan evaporation using soft computing tools

Estimation of evaporation plays a key role in managing water resources projects. Traditionally, evaporation is determined using theoretical and empirical techniques as well as by pan observations. Practically, it is difficult to install evaporation pans at every location and empirical approach like Penman’s equation is data intensive. This necessitates the use of an alternative approach, which can make use of readily available data and estimate evaporation reasonably with limited data. Major objective of the present study is to estimate evaporation using the soft computing tools of artificial neural networks (ANN) and genetic programming (GP) making use of the measured climatic parameters and to compare the results with traditional empirical techniques. The results indicate that the models developed using the soft computing tools of ANN and GP worked reasonably well for estimation of evaporation compared to empirical methods. GP works slightly better for higher values of pan evaporation compared to ANN.