Modeling Daily Pan Evaporation using a CCNN-GLM Hybrid Model

Evaporation estimate is an important topic in water resource management, especially in arid and semi-arid regions. In this paper, a hybrid model was developed to predict daily evaporation in a semi-arid region using two machine-learning algorithms, namely, the cascade correlation neural network (CCNN) algorithm and the generalized linear model (GLM) algorithm. Two types of inputs were used, the first was the daily evaporation time series data with different input patterns, and the second was daily climate data, which included maximum and minimum temperatures, maximum and minimum relative humidity, and wind speed. The results demonstrated the accuracy of the proposed hybrid model in estimating daily pan evaporation, with the coefficient of determination values reaching 0.95 and 0.93 for the training and validation periods, respectively.