A Time-Series Water Level Forecasting Model Based on Imputation and Variable Selection Method
Table 3
The results of listing models with the five imputation methods under percentage spilt (dataset partition into 66% training data and 34% testing data) before variable selection.
Methods
Index
RBF Network
Kstar
Random Forest
IBK
Random Tree
Before variable selection
Delete the rows with missing data
CC
0.085
0.546
0.728
0.288
0.534
MAE
0.183
0.133
0.111
0.186
0.145
RMSE
0.227
0.200
0.157
0.271
0.226
RAE
0.998
0.727
0.607
1.015
0.789
RRSE
0.997
0.879
0.690
1.193
0.995
Serial mean
CC
0.052
0.557
0.739
0.198
0.563
MAE
0.174
0.123
0.102
0.191
0.126
RMSE
0.222
0.189
0.151
0.283
0.202
RAE
1.001
0.705
0.587
1.098
0.722
RRSE
0.999
0.850
0.679
1.276
0.908
Linear
CC
0.054
0.565
0.734
0.200
0.512
MAE
0.175
0.121
0.101
0.189
0.138
RMSE
0.222
0.188
0.152
0.281
0.218
RAE
1.000
0.690
0.575
1.082
0.787
RRSE
0.999
0.844
0.684
1.264
0.980
Near median
CC
0.054
0.571
0.737
0.227
0.559
MAE
0.175
0.120
0.101
0.188
0.126
RMSE
0.222
0.186
0.152
0.277
0.202
RAE
1.000
0.689
0.577
1.074
0.719
RRSE
0.999
0.838
0.681
1.244
0.907
Near mean
CC
0.053
0.572
0.740
0.232
0.512
MAE
0.175
0.121
0.101
0.186
0.132
RMSE
0.222
0.186
0.151
0.275
0.217
RAE
1.000
0.690
0.575
1.062
0.756
RRSE
0.999
0.837
0.678
1.235
0.975
Regression
CC
0.052
0.564
0.739
0.200
0.509
MAE
0.174
0.121
0.102
0.191
0.133
RMSE
0.222
0.188
0.151
0.283
0.216
RAE
1.001
0.695
0.586
1.096
0.762
RRSE
0.999
0.845
0.680
1.275
0.974
denotes the best performance among 5 imputation methods.
