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Development and Validation of GANN Model for Evapotranspiration Estimation

2009; American Society of Civil Engineers; Volume: 14; Issue: 2 Linguagem: Inglês

10.1061/(asce)1084-0699(2009)14

1943-5584

Manoranjan Kumar, N. S. Raghuwanshi, Rajendra Prasad Singh,

Hydrological Forecasting Using AI

The present study was carried out to develop generalized artificial neural network (GANN) based reference crop evapotranspiration models corresponding to FAO-56 PM, FAO-24 Radiation, Turc, and FAO-24 Blaney–Criddle methods. The generalized ANN models were developed using the data from four California Irrigation Management Information System (CIMIS) stations, namely, Davis, Castroville, Mulberry, and West Side Field Station. The average weighted standard error of estimate (WSEE) for the developed models, namely, GANN (4-5-1), GANN (3-4-1), GANN (5-6-1), and GANN (6-7-1) corresponding to the FAO-24 Blaney–Criddle, FAO-24 Radiation, Turc, and FAO-56PM was 0.72, 0.85, 0.63, and 0.48mmday−1, respectively. The developed ANN models were applied at 2 CIMIS stations namely, Lodhi and Fresno, without any local training. The average WSEE for models GANN (4-5-1), GANN (3-4-1), GANN (5-6-1), and GANN (6-7-1) was 0.68, 0.71, 0.65, and 0.46mmday−1, respectively In addition, the GANN (4-5-1) model corresponding to FAO-24 Blaney–Criddle was directly applied to four Indian locations, namely, Hoshangabad, Gwalior, Jabalapur, and Pendra. The model gave the average WSEE of 0.57mmday−1. Based on the results it was concluded that the GANN models can be used directly to predict evapotranspiration (ETo) under the arid conditions, since they performed better than the conventional evapotranspiration (ETo) estimation method.