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

Table 4

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

Model	Training				Validation				Prediction
		NSE	RMSE	MAE		NSE	RMSE	MAE		NSE	RMSE	MAE
ANN	0.7593	0.7590	0.3791	0.2541	0.8553	0.7671	0.5690	0.3546	0.7859	0.7334	0.5911	0.3607
GRNN	0.8417	0.8176	0.3298	0.1997	0.8112	0.6472	0.7002	0.4096	0.7415	0.6307	0.6957	0.3919
GMDH	0.6673	0.6673	0.4454	0.3036	0.8303	0.6887	0.6577	0.4018	0.6931	0.6343	0.6922	0.4134
ELM-Sig	0.7159	0.7159	0.4115	0.2821	0.8038	0.7045	0.6409	0.4057	0.7469	0.6907	0.6367	0.3913
ELM-Sin	0.7163	0.7163	0.4113	0.2805	0.8423	0.7570	0.5811	0.3582	0.7530	0.6998	0.6272	0.3787
ELM-Hard	0.5615	0.5615	0.5113	0.3602	0.6843	0.5572	0.7845	0.4758	0.6013	0.5446	0.7725	0.4733
ANFIS-Grid	0.7820	0.7820	0.3605	0.2418	0.8480	0.7869	0.5443	0.3473	0.7805	0.7380	0.5859	0.3527
ANFIS-SC	0.7691	0.7691	0.3711	0.2495	0.8467	0.7712	0.5639	0.3507	0.7855	0.7311	0.5936	0.3492
ANFIS-FCM	0.7649	0.7649	0.3744	0.2532	0.8628	0.7879	0.5429	0.3405	0.7805	0.7287	0.5962	0.3554
SVM-RBF	0.7936	0.7912	0.3528	0.2111	0.8453	0.7668	0.5693	0.3418	0.7762	0.7225	0.6030	0.3577
SVM-Poly	0.6888	0.6772	0.4387	0.2888	0.8489	0.7824	0.5500	0.3326	0.7528	0.7053	0.6214	0.3676
SVM-Sig	0.0244	−0.0790	290.02	241.69	0.0616	−0.1655	263.96	225.25	0.0226	−0.0104	298.60	248.78

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