Skip to Main Content

Statistical performances of ELM, GRNN, and dual Kc models for maize ET estimation in 2013 are presented in Table 3. Based on the statistical indicators, ELM1 had the best performances for ET estimation, with RMSE of 0.221 mm/d, MAE of 0.203 mm/d, and NS of 0.981, respectively; estimated ET by ELM1 was 364.8 mm, which was 1.3% lower than measured ET. GRNN1 had good performances for ET estimation, too, with RMSE of 0.225 mm/d, MAE of 0.211 mm/d, and NS of 0.981, respectively; estimated ET by GRNN1 was 378.3 mm, which was 2.4% greater than measured ET. The performances of FAO-56 dual Kc approach were poorer than those of ELM1 and GRNN1, but better than those of ELM2 and GRNN2, with RMSE of 0.381 mm/d, MAE of 0.332 mm/d, and NS of 0.871, respectively. Although ELM2 and GRNN2 were not as efficient as ELM1, GRNN1, and dual Kc models, their estimation of ET was acceptable when only meteorological data were available.

Table 3

Statistical performances of ELM, GRNN, and dual Kc models for maize ET estimation in 2013

ModelEstimated ET (mm)Over/Underestimation (%)RMSE (mm/d)MAE (mm/d)NS
ELM1 364.8 −1.3 0.221 0.203 0.981 
GRNN1 378.3 2.4 0.225 0.211 0.981 
ELM2 398.6 7.9 0.403 0.353 0.848 
GRNN2 400.8 8.5 0.521 0.421 0.836 
FAO-56 385.6 4.4 0.381 0.332 0.871 
ModelEstimated ET (mm)Over/Underestimation (%)RMSE (mm/d)MAE (mm/d)NS
ELM1 364.8 −1.3 0.221 0.203 0.981 
GRNN1 378.3 2.4 0.225 0.211 0.981 
ELM2 398.6 7.9 0.403 0.353 0.848 
GRNN2 400.8 8.5 0.521 0.421 0.836 
FAO-56 385.6 4.4 0.381 0.332 0.871 

Close Modal

or Create an Account

Close Modal
Close Modal