Skip to main content

Advertisement

Science – Society – Technology

Geothermal Energy Cover Image

Table 3 Testing RMSE (\(E_{\text {R}}\)), NRMSE (\(E_{\text {NR}}\), in percentage) and OOB score for Random Forest models trained for monthly ground temperature at multiple depths

From: A machine learning approach for mapping the very shallow theoretical geothermal potential

Month5 cm10 cm20 cm50 cm100 cm
ER (°C)ENR (%)OOB (−)ER (°C)ENR (%)OOB (−)ER (°C)ENR (%)OOB (−)ER (°C)ENR (%)OOB (−)ER (°C)ENR (%)OOB (−)
Jan.0.8125.400.170.6920.810.460.5719.180.430.6323.170.250.8717.550.26
Feb.0.5718.690.150.6520.200.480.6922.240.340.6626.880.270.8620.270.24
Mar.0.7813.740.350.9416.240.461.3420.440.401.3235.380.401.0420.580.44
Apr.0.8808.490.591.1210.620.461.3512.300.462.0032.110.441.1115.360.52
May.1.4810.380.520.9706.620.341.3508.860.341.8017.210.331.1911.580.47
Jun.2.3412.480.561.2206.410.421.2206.350.422.0214.620.311.3409.950.47
Jul.2.2210.550.461.0805.060.481.1805.590.402.2113.770.251.5409.740.40
Aug.1.6808.280.531.2205.950.480.9604.620.462.0912.470.291.5108.880.37
Sep.1.1006.570.461.0205.860.451.0505.990.381.9012.900.231.4409.030.42
Oct.0.8306.610.370.8706.660.340.7105.430.281.5713.650.331.1208.250.46
Nov.0.9311.900.240.8610.440.320.6007.180.311.1916.350.301.1411.250.38
Dec.1.1029.080.060.7818.890.350.6014.270.430.8019.530.321.0515.310.39