GMS location: 381

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.986 0.000e+00 0.347 0.442 1.712 NaN NaN
forest winter 2016 1.000 0.100 0.292 0.423 1.408 0.417 1.998
baseline winter 2017 0.966 0.118 0.633 0.558 2.985 NaN NaN
forest winter 2017 0.975 0.147 0.520 0.501 2.885 0.434 3.184
baseline winter 2018 0.986 0.125 0.359 0.460 1.961 NaN NaN
forest winter 2018 0.993 0.125 0.337 0.440 2.048 0.415 1.511
baseline winter 2019 0.984 0.000e+00 0.355 0.441 2.195 NaN NaN
forest winter 2019 0.984 0.000e+00 0.256 0.376 1.815 0.414 1.500
baseline all 0.981 0.076 0.422 0.475 2.985 NaN NaN
forest all 0.989 0.109 0.351 0.436 2.885 0.420 2.047

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.986 0.000e+00 0.347 0.442 1.712 NaN NaN
elr winter 2016 0.993 0.100 0.323 0.454 1.502 0.441 2.076
baseline winter 2017 0.966 0.118 0.633 0.558 2.985 NaN NaN
elr winter 2017 0.966 0.147 0.571 0.531 3.199 0.442 2.630
baseline winter 2018 0.986 0.125 0.359 0.460 1.961 NaN NaN
elr winter 2018 0.993 0.125 0.374 0.477 2.102 0.466 2.252
baseline winter 2019 0.984 0.000e+00 0.355 0.441 2.195 NaN NaN
elr winter 2019 0.984 0.000e+00 0.317 0.425 1.967 0.461 2.083
baseline all 0.981 0.076 0.422 0.475 2.985 NaN NaN
elr all 0.985 0.109 0.396 0.472 3.199 0.452 2.260

Extended logistic regression plots

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