GMS location: 421

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.982 0.154 0.313 0.406 2.099 NaN NaN
forest winter 2016 0.982 0.154 0.263 0.361 2.027 0.493 2.982
baseline winter 2017 0.970 0.048 0.496 0.491 3.043 NaN NaN
forest winter 2017 0.960 0.048 0.356 0.429 2.041 0.483 2.970
baseline winter 2018 0.989 0.083 0.379 0.405 3.115 NaN NaN
forest winter 2018 0.979 0.056 0.326 0.394 3.029 0.492 3.478
baseline winter 2019 0.990 0.000e+00 0.342 0.397 2.355 NaN NaN
forest winter 2019 0.990 0.050 0.234 0.352 1.661 0.493 2.599
baseline all 0.983 0.073 0.378 0.424 3.115 NaN NaN
forest all 0.978 0.073 0.293 0.383 3.029 0.490 3.008

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.982 0.154 0.313 0.406 2.099 NaN NaN
elr winter 2016 0.982 0.077 0.270 0.389 1.923 0.566 3.384
baseline winter 2017 0.970 0.048 0.496 0.491 3.043 NaN NaN
elr winter 2017 0.970 0.024 0.343 0.443 1.973 0.531 3.218
baseline winter 2018 0.989 0.083 0.379 0.405 3.115 NaN NaN
elr winter 2018 0.979 0.028 0.337 0.404 2.883 0.541 3.904
baseline winter 2019 0.990 0.000e+00 0.342 0.397 2.355 NaN NaN
elr winter 2019 0.990 0.150 0.291 0.408 1.702 0.565 3.464
baseline all 0.983 0.073 0.378 0.424 3.115 NaN NaN
elr all 0.981 0.057 0.307 0.409 2.883 0.552 3.476

Extended logistic regression plots

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