GMS location: 413

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.062 0.420 0.497 2.448 NaN NaN
forest winter 2016 0.981 0.062 0.303 0.412 1.932 0.447 2.954
baseline winter 2017 0.972 0.023 0.550 0.537 2.808 NaN NaN
forest winter 2017 0.962 0.023 0.349 0.451 1.606 0.425 2.590
baseline winter 2018 0.992 0.108 0.399 0.451 2.713 NaN NaN
forest winter 2018 0.992 0.108 0.380 0.448 2.893 0.453 3.228
baseline winter 2019 0.991 0.040 0.477 0.472 3.526 NaN NaN
forest winter 2019 1.000 0.080 0.334 0.409 2.298 0.429 2.254
baseline all 0.988 0.058 0.457 0.489 3.526 NaN NaN
forest all 0.984 0.065 0.341 0.430 2.893 0.440 2.789

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 0.062 0.420 0.497 2.448 NaN NaN
elr winter 2016 0.981 0.031 0.413 0.488 2.445 0.536 3.357
baseline winter 2017 0.972 0.023 0.550 0.537 2.808 NaN NaN
elr winter 2017 0.981 0.068 0.438 0.483 2.546 0.449 2.392
baseline winter 2018 0.992 0.108 0.399 0.451 2.713 NaN NaN
elr winter 2018 1.000 0.081 0.373 0.453 3.012 0.542 3.305
baseline winter 2019 0.991 0.040 0.477 0.472 3.526 NaN NaN
elr winter 2019 1.000 0.160 0.440 0.453 2.868 0.490 2.995
baseline all 0.988 0.058 0.457 0.489 3.526 NaN NaN
elr all 0.990 0.080 0.414 0.470 3.012 0.507 3.039

Extended logistic regression plots

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