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

Table 6

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

Model	Training				Validation				Prediction
		NSE	RMSE	MAE		NSE	RMSE	MAE		NSE	RMSE	MAE
ANN	0.8765	0.8764	0.3955	0.2636	0.8038	0.8023	0.4474	0.3022	0.7969	0.7907	0.5018	0.3635
GRNN	0.9043	0.9041	0.3483	0.2284	0.7412	0.7333	0.5196	0.3581	0.7519	0.7395	0.5598	0.3895
GMDH	0.8135	0.8135	0.4858	0.3303	0.7773	0.7770	0.4752	0.3315	0.8165	0.8143	0.4727	0.3272
ELM-Sig	0.8467	0.8467	0.4405	0.3030	0.7625	0.7563	0.4967	0.3566	0.8155	0.8087	0.4797	0.3402
ELM-Sin	0.8521	0.8521	0.4327	0.2910	0.7823	0.7807	0.4712	0.3352	0.8134	0.8075	0.4813	0.3449
ELM-Hard	0.7113	0.7113	0.6045	0.4539	0.6425	0.6291	0.6128	0.4577	0.7045	0.7017	0.5991	0.4478
ANFIS-Grid	0.8921	0.8921	0.3695	0.2556	0.7178	0.7008	0.5504	0.3605	0.7595	0.7414	0.5578	0.3774
ANFIS-SC	0.8721	0.8721	0.4024	0.2705	0.7761	0.7713	0.4811	0.3407	0.7893	0.7784	0.5164	0.3752
ANFIS-FCM	0.8679	0.8679	0.4089	0.2760	0.7709	0.7655	0.4873	0.3376	0.7726	0.7636	0.5333	0.3801
SVM-RBF	0.8917	0.8897	0.3737	0.2323	0.7529	0.7511	0.5020	0.3404	0.7699	0.7671	0.5294	0.3739
SVM-Poly	0.7451	0.7392	0.5745	0.3901	0.7344	0.7286	0.5241	0.3737	0.7266	0.7237	0.5765	0.4114
SVM-Sig	0.0049	−0.0610	218.15	178.95	0.0193	−0.0991	201.38	168.64	0.0090	−0.0053	207.32	171.67

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