Pan evaporation modeling in different agroclimatic zones using functional link artificial neural network

Pan evaporation is an important climatic variable for developing efficient water resource management strategies. In the past, many machine learning models are reported in the literature for pan evaporation modeling using the different combinationof available climatic variables. In order to develop a novel model with improved accuracy and reduced computational complexity, the functional link artificial neural network (FLANN) is chosen as an architecture to estimate daily pan evaporation in three agro-climatic zones (ACZs) of Chhattisgarh state in east-central India. Single neuron and single layer in its structure make it less complex as compared to other multilayer neural networks and neuro-fuzzy based hybrid models. Estimation results obtained with the FLANN model are compared with those obtained by multi-layer artificial neural networks (MLANN) and two empirical methods using the same raw data and corresponding features. Statistical indices like root mean square error (RMSE), mean absolute error (MAE) and efficiency factor (EF) is also computed to evaluate the model performance. It is demonstrated that pan evaporation estimates obtained with the proposed FLANN models provide an improved estimation of pan evaporation (RMSE = 0.85 to 1.27 m m d - 1 , MAE = 0.63 to 0.95 m m d - 1 and EF = 0.70 to 0.89) as compared to MLANN (RMSE = 0.94 to 1.58 m m d - 1 , MAE = 0.73 to 1.14 m m d - 1 and EF = 0.62 to 0.88) and empirical (RMSE = 1.19 to 2.19 m m d - 1 , MAE = 0.91 to 1.62 m m d - 1 and EF = 0.49 to 0.88) models in different ACZs.