GMS location: 217

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.000e+00 0.348 0.449 2.137 NaN NaN
forest winter 2016 0.982 0.000e+00 0.281 0.395 1.914 0.534 5.587
baseline winter 2017 0.956 0.077 0.346 0.446 2.156 NaN NaN
forest winter 2017 0.939 0.051 0.279 0.390 1.625 0.516 4.836
baseline winter 2018 0.993 0.167 0.340 0.428 1.712 NaN NaN
forest winter 2018 0.993 0.167 0.281 0.393 1.916 0.537 5.039
baseline winter 2019 0.985 0.071 0.278 0.361 2.211 NaN NaN
forest winter 2019 0.993 0.000e+00 0.204 0.320 1.750 0.532 3.975
baseline all 0.984 0.083 0.330 0.423 2.211 NaN NaN
forest all 0.978 0.064 0.263 0.377 1.916 0.530 4.914

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.000e+00 0.348 0.449 2.137 NaN NaN
elr winter 2016 0.988 0.000e+00 0.317 0.438 1.706 0.627 6.890
baseline winter 2017 0.956 0.077 0.346 0.446 2.156 NaN NaN
elr winter 2017 0.965 0.051 0.283 0.394 1.778 0.545 4.685
baseline winter 2018 0.993 0.167 0.340 0.428 1.712 NaN NaN
elr winter 2018 0.985 0.167 0.344 0.448 2.163 0.597 5.939
baseline winter 2019 0.985 0.071 0.278 0.361 2.211 NaN NaN
elr winter 2019 0.993 0.071 0.227 0.350 1.490 0.555 4.181
baseline all 0.984 0.083 0.330 0.423 2.211 NaN NaN
elr all 0.984 0.073 0.296 0.410 2.163 0.584 5.532

Extended logistic regression plots

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