GMS location: 1103

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.133 0.341 0.422 2.077 NaN NaN
forest winter 2016 0.977 0.067 0.286 0.386 1.927 0.450 3.723
baseline winter 2017 0.983 0.105 0.560 0.514 2.539 NaN NaN
forest winter 2017 0.991 0.053 0.363 0.413 2.283 0.478 4.984
baseline winter 2018 0.986 0.105 0.397 0.455 1.975 NaN NaN
forest winter 2018 1.000 0.105 0.331 0.415 2.132 0.456 3.802
baseline winter 2019 0.981 0.000e+00 0.259 0.366 1.829 NaN NaN
forest winter 2019 0.994 0.000e+00 0.214 0.342 1.457 0.451 3.180
baseline all 0.985 0.102 0.382 0.437 2.539 NaN NaN
forest all 0.990 0.068 0.297 0.389 2.283 0.458 3.881

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.133 0.341 0.422 2.077 NaN NaN
elr winter 2016 0.989 0.067 0.296 0.412 1.901 0.515 3.726
baseline winter 2017 0.983 0.105 0.560 0.514 2.539 NaN NaN
elr winter 2017 0.983 0.079 0.472 0.485 2.420 0.513 4.638
baseline winter 2018 0.986 0.105 0.397 0.455 1.975 NaN NaN
elr winter 2018 0.993 0.053 0.366 0.438 2.393 0.534 4.399
baseline winter 2019 0.981 0.000e+00 0.259 0.366 1.829 NaN NaN
elr winter 2019 0.988 0.000e+00 0.250 0.381 1.730 0.510 3.325
baseline all 0.985 0.102 0.382 0.437 2.539 NaN NaN
elr all 0.988 0.059 0.341 0.427 2.420 0.518 3.996

Extended logistic regression plots

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