Modeling Evapotranspiration Response to Climatic Forcings Using Data-Driven Techniques in Grassland Ecosystems

Table 5

Comparisons of data-driven model performances for evapotranspiration among the training, validation, and prediction periods at DE-Gri site.

Model	Training				Validation				Prediction
		NSE	RMSE	MAE		NSE	RMSE	MAE		NSE	RMSE	MAE
ANN	0.8633	0.8630	0.3470	0.2426	0.9341	0.8927	0.3411	0.2336	0.9623	0.9341	0.2769	0.1942
GRNN	0.8612	0.8603	0.3504	0.2487	0.9294	0.8814	0.3587	0.2478	0.9585	0.9292	0.2871	0.2058
GMDH	0.8396	0.8396	0.3755	0.2707	0.9326	0.8724	0.3719	0.2573	0.9619	0.9253	0.2949	0.2121
ELM-Sig	0.8535	0.8535	0.3589	0.2580	0.9358	0.8885	0.3478	0.2347	0.9669	0.9392	0.2659	0.1920
ELM-Sin	0.8546	0.8546	0.3575	0.2522	0.9477	0.9066	0.3182	0.2203	0.9711	0.9398	0.2646	0.1896
ELM-Hard	0.8012	0.8012	0.4180	0.2911	0.8926	0.8724	0.3720	0.2662	0.9053	0.8765	0.3791	0.2534
ANFIS-Grid	0.8772	0.8772	0.3285	0.2304	0.9173	0.8713	0.3735	0.2447	0.9574	0.9334	0.2785	0.1961
ANFIS-SC	0.8637	0.8637	0.3461	0.2427	0.9230	0.8716	0.3732	0.2397	0.9501	0.9088	0.3258	0.2167
ANFIS-FCM	0.8636	0.8636	0.3462	0.2439	0.9244	0.8744	0.3691	0.2378	0.9500	0.9099	0.3238	0.2159
SVM-RBF	0.8638	0.8633	0.3466	0.2306	0.9196	0.8796	0.3614	0.2332	0.9407	0.9002	0.3408	0.2092
SVM-Poly	0.7885	0.7852	0.4345	0.3221	0.7629	0.7043	0.5662	0.3879	0.8461	0.8199	0.4578	0.3407
SVM-Sig	0.0170	−0.0682	43.738	37.467	0.0001	−0.3639	37.708	31.554	0.1190	−0.2045	45.388	39.515

Note. The unit of RMSE and MAE is mm day⁻¹.