GMS location: 453

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.977 0.143 0.354 0.463 1.839 NaN NaN
forest winter 2016 0.994 0.143 0.232 0.373 1.471 0.437 7.321
baseline winter 2017 0.976 0.103 0.385 0.456 2.186 NaN NaN
forest winter 2017 0.976 0.172 0.215 0.354 1.137 0.432 4.546
baseline winter 2018 1.000 NaN 0.245 0.360 1.272 NaN NaN
forest winter 2018 1.000 NaN 0.190 0.328 1.038 0.432 3.355
baseline winter 2019 0.990 0.000e+00 0.330 0.420 2.457 NaN NaN
forest winter 2019 1.000 0.077 0.212 0.353 1.238 0.426 4.786
baseline all 0.981 0.089 0.351 0.444 2.457 NaN NaN
forest all 0.991 0.143 0.219 0.359 1.471 0.432 5.593

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.977 0.143 0.354 0.463 1.839 NaN NaN
elr winter 2016 0.988 0.143 0.270 0.423 1.374 0.509 6.564
baseline winter 2017 0.976 0.103 0.385 0.456 2.186 NaN NaN
elr winter 2017 0.984 0.103 0.234 0.363 1.522 0.496 6.582
baseline winter 2018 1.000 NaN 0.245 0.360 1.272 NaN NaN
elr winter 2018 1.000 NaN 0.195 0.336 1.182 0.504 6.681
baseline winter 2019 0.990 0.000e+00 0.330 0.420 2.457 NaN NaN
elr winter 2019 1.000 0.077 0.239 0.384 1.278 0.507 6.456
baseline all 0.981 0.089 0.351 0.444 2.457 NaN NaN
elr all 0.991 0.107 0.246 0.389 1.522 0.504 6.553

Extended logistic regression plots

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