GMS location: 1161

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.028 0.315 0.406 2.082 NaN NaN
forest winter 2016 0.977 0.028 0.330 0.429 2.104 0.564 3.810
baseline winter 2017 0.979 0.048 0.399 0.467 1.841 NaN NaN
forest winter 2017 0.979 0.048 0.340 0.439 1.563 0.535 3.672
baseline winter 2018 0.976 0.109 0.430 0.476 2.587 NaN NaN
forest winter 2018 0.976 0.152 0.375 0.465 2.111 0.560 4.220
baseline winter 2019 0.992 0.000e+00 0.343 0.415 1.927 NaN NaN
forest winter 2019 0.992 0.053 0.263 0.374 1.793 0.545 3.800
baseline all 0.987 0.056 0.368 0.439 2.587 NaN NaN
forest all 0.981 0.077 0.329 0.428 2.111 0.553 3.886

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 0.028 0.315 0.406 2.082 NaN NaN
elr winter 2016 0.982 0.028 0.351 0.461 1.935 0.638 5.513
baseline winter 2017 0.979 0.048 0.399 0.467 1.841 NaN NaN
elr winter 2017 0.979 0.024 0.379 0.465 1.728 0.594 4.952
baseline winter 2018 0.976 0.109 0.430 0.476 2.587 NaN NaN
elr winter 2018 0.953 0.109 0.373 0.463 2.227 0.606 5.010
baseline winter 2019 0.992 0.000e+00 0.343 0.415 1.927 NaN NaN
elr winter 2019 0.992 0.053 0.293 0.402 1.709 0.587 4.628
baseline all 0.987 0.056 0.368 0.439 2.587 NaN NaN
elr all 0.977 0.056 0.349 0.449 2.227 0.609 5.067

Extended logistic regression plots

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