A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method

Table 4

The results of listing models with the five imputation methods under 10-folds cross-validation before variable selection.

	Methods	Index	RBF Network	Kstar	Random Forest	IBK	Random Tree
Before variable selection	Delete the rows with missing data	CC	0.041	0.590	0.737	0.246	0.505
		MAE	0.184	0.126	0.109	0.195	0.143
		RMSE	0.227	0.188	0.154	0.281	0.225
		RAE	1.000	0.682	0.592	1.059	0.775
		RRSE	0.999	0.825	0.678	1.235	0.986
	Serial mean	CC	0.038	0.612	0.755	0.237	0.574
		MAE	0.171	0.113	0.098	0.181	0.125
		RMSE	0.217	0.175	0.143	0.270	0.202
		RAE	1.001	0.660	0.575	1.058	0.731
		RRSE	0.999	0.802	0.658	1.241	0.929
	Linear	CC	0.042	0.615	0.753	0.243	0.551
		MAE	0.173	0.112	0.098	0.181	0.127
		RMSE	0.218	0.175	0.144	0.269	0.207
		RAE	1.000	0.649	0.568	1.057	0.736
		RRSE	0.999	0.800	0.660	1.233	0.948
	Near median	CC	0.043	0.614	0.752	0.251	0.535
		MAE	0.173	0.113	0.098	0.180	0.131
		RMSE	0.218	0.175	0.144	0.268	0.211
		RAE	1.000	0.653	0.568	1.041	0.757
		RRSE	0.999	0.801	0.661	1.227	0.967
	Near mean	CC	0.043	0.613	0.756	0.250	0.558
		MAE	0.173	0.113	0.098	0.179	0.125
		RMSE	0.218	0.175	0.144	0.268	0.205
		RAE	1.000	0.654	0.565	1.039	0.725
		RRSE	0.999	0.802	0.657	1.226	0.937
	Regression	CC	0.038	0.618	0.754	0.240	0.522
		MAE	0.171	0.112	0.098	0.181	0.133
		RMSE	0.217	0.174	0.143	0.270	0.214
		RAE	1.001	0.653	0.574	1.055	0.778
		RRSE	0.999	0.798	0.659	1.239	0.983

denotes the best performance among 5 imputation methods.