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

Table 3

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

Model	Training				Validation				Prediction
		NSE	RMSE	MAE		NSE	RMSE	MAE		NSE	RMSE	MAE
ANN	0.9298	0.9294	0.3551	0.2321	0.9407	0.9375	0.3318	0.2203	0.9355	0.9338	0.3350	0.2278
GRNN	0.9596	0.9595	0.2689	0.1635	0.9134	0.9118	0.3943	0.2640	0.9207	0.9186	0.3714	0.2473
GMDH	0.9271	0.9270	0.3610	0.2442	0.9246	0.9220	0.3707	0.2464	0.9356	0.9320	0.3394	0.2342
ELM-Sig	0.9256	0.9256	0.3644	0.2430	0.9265	0.9249	0.3639	0.2461	0.9355	0.9328	0.3376	0.2322
ELM-Sin	0.9278	0.9278	0.3591	0.2332	0.9302	0.9283	0.3554	0.2415	0.9365	0.9344	0.3335	0.2242
ELM-Hard	0.8762	0.8762	0.4702	0.3365	0.8814	0.8811	0.4577	0.3394	0.9043	0.9031	0.4053	0.2836
ANFIS-Grid	0.9407	0.9407	0.3255	0.2121	0.9237	0.9230	0.3684	0.2585	0.9247	0.9219	0.3639	0.2436
ANFIS-SC	0.9373	0.9373	0.3347	0.2138	0.9318	0.9304	0.3503	0.2402	0.9379	0.9355	0.3308	0.2260
ANFIS-FCM	0.9376	0.9376	0.3338	0.2151	0.9380	0.9358	0.3363	0.2314	0.9369	0.9343	0.3338	0.2282
SVM-RBF	0.9387	0.9373	0.3347	0.1928	0.9333	0.9328	0.3440	0.2357	0.9338	0.9321	0.3392	0.2286
SVM-Poly	0.8357	0.8321	0.5476	0.4007	0.8351	0.8341	0.5406	0.4287	0.8746	0.8696	0.4701	0.3579
SVM-Sig	0.5948	0.5061	0.9391	0.7203	0.5113	0.4352	0.9976	0.7997	0.5910	0.5063	0.9149	0.7275

Note. The units of RMSE and MAE are mm day⁻¹.