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Table 2

Averaged modeling results for groundwater levels for five different prediction horizons (from 1 to 5 days ahead)

1 day ahead
2 days ahead
3 days ahead
4 days ahead
5 days ahead
MethodR2σR2σR2σR2σR2σtt (ms)tp (ms)
LinearRegression 0.834 0.037 0.708 0.063 0.661 0.066 0.638 0.063 0.641 0.07 1.9 0.2 
DecisionTreeR 0.677 0.146 0.61 0.111 0.545 0.093 0.541 0.061 0.543 0.059 3.3 0.1 
RandomForestR 0.842 0.031 0.726 0.108 0.669 0.08 0.644 0.067 0.600 0.054 1,047 8.5 
GradientBoostingR 0.849 0.037 0.775 0.052 0.732 0.071 0.690 0.081 0.655 0.09 322.6 0.5 
PLSRegression 0.726 0.052 0.65 0.073 0.639 0.076 0.639 0.076 0.639 0.077 2.9 <0.1 
ExtraTreeR 0.677 0.146 0.61 0.111 0.545 0.093 0.541 0.061 0.543 0.059 3.3 <0.1 
SVR − 0.137 0.41 − 0.16 0.356 − 0.113 0.325 − 0.069 0.317 − 0.086 0.324 2.5 0.4 
MLP-R 0.825 0.045 0.691 0.084 0.659 0.09 0.646 0.092 0.641 0.082 210.2 1.3 
KNeighborsR 0.747 0.053 0.661 0.089 0.631 0.102 0.618 0.112 0.610 0.114 5.9 8.0 
HoeffdingTreeR 0.495 0.164 0.513 0.127 0.518 0.144 0.493 0.139 0.482 0.13 3,347.1 28.1 
HAT-R 0.506 0.176 0.513 0.127 0.518 0.144 0.493 0.139 0.482 0.13 3,722.2 28.5 
LogisticRegression 0.485 0.153 0.408 0.131 0.395 0.146 0.376 0.179 0.370 0.16 83.3 0.2 
DecisionTreeC 0.506 0.141 0.395 0.116 0.342 0.183 0.365 0.203 0.377 0.183 4.9 <0.1 
ExtraTreeC 0.306 0.09 0.336 0.113 0.24 0.162 0.332 0.058 0.256 0.258 2.4 0.1 
RandomForestC 0.554 0.108 0.489 0.178 0.481 0.19 0.498 0.176 0.470 0.186 279.8 9.8 
SVC 0.522 0.088 0.413 0.131 0.375 0.158 0.391 0.193 0.400 0.191 135.4 16.9 
KNeighborsC 0.530 0.071 0.378 0.143 0.387 0.201 0.357 0.157 0.353 0.16 7.8 15.8 
Perceptron 0.435 0.206 0.102 0.388 − 0.007 0.788 0.325 0.291 0.266 0.268 10.5 0.2 
GaussianNB 0.472 0.112 0.378 0.123 0.374 0.138 0.362 0.144 0.358 0.148 1.2 0.3 
HoeffdingTreeC 0.472 0.112 0.378 0.123 0.374 0.138 0.362 0.144 0.358 0.148 1,054.2 119.5 
HAT-C 0.453 0.108 0.371 0.14 0.364 0.153 0.346 0.147 0.353 0.148 1,912.2 120.4 
1 day ahead
2 days ahead
3 days ahead
4 days ahead
5 days ahead
MethodR2σR2σR2σR2σR2σtt (ms)tp (ms)
LinearRegression 0.834 0.037 0.708 0.063 0.661 0.066 0.638 0.063 0.641 0.07 1.9 0.2 
DecisionTreeR 0.677 0.146 0.61 0.111 0.545 0.093 0.541 0.061 0.543 0.059 3.3 0.1 
RandomForestR 0.842 0.031 0.726 0.108 0.669 0.08 0.644 0.067 0.600 0.054 1,047 8.5 
GradientBoostingR 0.849 0.037 0.775 0.052 0.732 0.071 0.690 0.081 0.655 0.09 322.6 0.5 
PLSRegression 0.726 0.052 0.65 0.073 0.639 0.076 0.639 0.076 0.639 0.077 2.9 <0.1 
ExtraTreeR 0.677 0.146 0.61 0.111 0.545 0.093 0.541 0.061 0.543 0.059 3.3 <0.1 
SVR − 0.137 0.41 − 0.16 0.356 − 0.113 0.325 − 0.069 0.317 − 0.086 0.324 2.5 0.4 
MLP-R 0.825 0.045 0.691 0.084 0.659 0.09 0.646 0.092 0.641 0.082 210.2 1.3 
KNeighborsR 0.747 0.053 0.661 0.089 0.631 0.102 0.618 0.112 0.610 0.114 5.9 8.0 
HoeffdingTreeR 0.495 0.164 0.513 0.127 0.518 0.144 0.493 0.139 0.482 0.13 3,347.1 28.1 
HAT-R 0.506 0.176 0.513 0.127 0.518 0.144 0.493 0.139 0.482 0.13 3,722.2 28.5 
LogisticRegression 0.485 0.153 0.408 0.131 0.395 0.146 0.376 0.179 0.370 0.16 83.3 0.2 
DecisionTreeC 0.506 0.141 0.395 0.116 0.342 0.183 0.365 0.203 0.377 0.183 4.9 <0.1 
ExtraTreeC 0.306 0.09 0.336 0.113 0.24 0.162 0.332 0.058 0.256 0.258 2.4 0.1 
RandomForestC 0.554 0.108 0.489 0.178 0.481 0.19 0.498 0.176 0.470 0.186 279.8 9.8 
SVC 0.522 0.088 0.413 0.131 0.375 0.158 0.391 0.193 0.400 0.191 135.4 16.9 
KNeighborsC 0.530 0.071 0.378 0.143 0.387 0.201 0.357 0.157 0.353 0.16 7.8 15.8 
Perceptron 0.435 0.206 0.102 0.388 − 0.007 0.788 0.325 0.291 0.266 0.268 10.5 0.2 
GaussianNB 0.472 0.112 0.378 0.123 0.374 0.138 0.362 0.144 0.358 0.148 1.2 0.3 
HoeffdingTreeC 0.472 0.112 0.378 0.123 0.374 0.138 0.362 0.144 0.358 0.148 1,054.2 119.5 
HAT-C 0.453 0.108 0.371 0.14 0.364 0.153 0.346 0.147 0.353 0.148 1,912.2 120.4 

Best results by prediction horizon are bolded. Best classification-based results are underlined.

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