GMS location: 425

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.107 0.284 0.396 2.300 NaN NaN
forest winter 2016 0.966 0.036 0.276 0.391 2.173 0.525 4.121
baseline winter 2017 0.991 0.046 0.448 0.468 2.151 NaN NaN
forest winter 2017 0.982 0.093 0.356 0.440 1.988 0.484 3.696
baseline winter 2018 0.992 0.103 0.337 0.408 1.900 NaN NaN
forest winter 2018 0.992 0.103 0.315 0.407 1.713 0.502 3.163
baseline winter 2019 0.981 0.000e+00 0.372 0.446 2.631 NaN NaN
forest winter 2019 0.981 0.062 0.302 0.392 2.151 0.508 3.172
baseline all 0.988 0.071 0.354 0.427 2.631 NaN NaN
forest all 0.979 0.079 0.309 0.406 2.173 0.506 3.563

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 0.107 0.284 0.396 2.300 NaN NaN
elr winter 2016 0.972 0.000e+00 0.369 0.465 2.180 0.612 4.322
baseline winter 2017 0.991 0.046 0.448 0.468 2.151 NaN NaN
elr winter 2017 0.991 0.070 0.399 0.457 2.085 0.532 4.302
baseline winter 2018 0.992 0.103 0.337 0.408 1.900 NaN NaN
elr winter 2018 0.992 0.103 0.366 0.467 1.706 0.560 4.182
baseline winter 2019 0.981 0.000e+00 0.372 0.446 2.631 NaN NaN
elr winter 2019 0.981 0.062 0.385 0.467 2.352 0.535 3.455
baseline all 0.988 0.071 0.354 0.427 2.631 NaN NaN
elr all 0.983 0.064 0.379 0.464 2.352 0.563 4.067

Extended logistic regression plots

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