GMS location: 854

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.973 0.091 0.347 0.423 2.287 NaN NaN
forest winter 2016 0.973 0.182 0.338 0.419 2.156 0.578 1.635
baseline winter 2017 0.991 0.103 2.691 0.672 1.420e+01 NaN NaN
forest winter 2017 0.982 0.103 2.606 0.673 1.407e+01 0.472 2.214
baseline winter 2018 0.993 0.121 0.504 0.477 3.993 NaN NaN
forest winter 2018 0.985 0.151 0.580 0.524 3.908 0.662 1.998
baseline winter 2019 1.000 0.000e+00 0.262 0.369 1.701 NaN NaN
forest winter 2019 1.000 0.000e+00 0.225 0.361 1.330 0.598 1.657
baseline all 0.988 0.085 0.888 0.480 1.420e+01 NaN NaN
forest all 0.984 0.110 0.877 0.488 1.407e+01 0.580 1.859

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.973 0.091 0.347 0.423 2.287 NaN NaN
elr winter 2016 0.957 0.091 0.357 0.445 2.230 0.491 1.352
baseline winter 2017 0.991 0.103 2.691 0.672 1.420e+01 NaN NaN
elr winter 2017 0.991 0.077 2.615 0.702 1.398e+01 0.510 2.802
baseline winter 2018 0.993 0.121 0.504 0.477 3.993 NaN NaN
elr winter 2018 0.985 0.121 0.511 0.507 3.857 0.459 1.321
baseline winter 2019 1.000 0.000e+00 0.262 0.369 1.701 NaN NaN
elr winter 2019 1.000 0.000e+00 0.248 0.377 1.465 0.434 1.180
baseline all 0.988 0.085 0.888 0.480 1.420e+01 NaN NaN
elr all 0.981 0.076 0.872 0.502 1.398e+01 0.474 1.628

Extended logistic regression plots

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