Application of Artificial Intelligence to Estimate Daily Pan Evaporation Using Available and Estimated Climatic Data in the Khozestan Province (South Western Iran)

Abstract

Estimation of evaporation, a major component of the hydrologic cycle, is required for a variety of purposes in water resources development and management. This paper investigates the abilities of genetic programming (GP) to improve the accuracy of daily evaporation estimation. In the first part of the study, different GP models, comprising various combinations of daily climatic variables, namely, air temperature, sunshine hours, wind speed, and relative humidity, were developed to evaluate the degree of the effect of each variable on daily pan evaporation. A dynamic modeling of evaporation was also performed, with the current climatic variables and one of the previous variables, to evaluate the effect of their time series on evaporation. In the second part of the study, the estimated solar radiation data were used as input vectors instead of recorded sunshine values. Statistics such as correlation coefficient (