GMS location: 471

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.087 0.365 0.447 2.114 NaN NaN
forest winter 2016 0.989 0.130 0.277 0.393 1.654 0.454 2.817
baseline winter 2017 0.974 0.081 0.483 0.503 2.690 NaN NaN
forest winter 2017 0.966 0.081 0.341 0.419 2.029 0.443 3.219
baseline winter 2018 0.980 0.065 0.336 0.418 2.696 NaN NaN
forest winter 2018 0.987 0.065 0.326 0.398 2.655 0.440 2.580
baseline winter 2019 0.986 0.067 0.310 0.409 2.092 NaN NaN
forest winter 2019 0.993 0.067 0.205 0.345 1.509 0.440 2.613
baseline all 0.985 0.075 0.371 0.443 2.696 NaN NaN
forest all 0.985 0.085 0.287 0.389 2.655 0.445 2.797

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.087 0.365 0.447 2.114 NaN NaN
elr winter 2016 0.989 0.130 0.329 0.448 1.886 0.513 3.590
baseline winter 2017 0.974 0.081 0.483 0.503 2.690 NaN NaN
elr winter 2017 0.983 0.081 0.381 0.439 2.182 0.468 3.059
baseline winter 2018 0.980 0.065 0.336 0.418 2.696 NaN NaN
elr winter 2018 0.993 0.065 0.339 0.410 2.796 0.495 3.324
baseline winter 2019 0.986 0.067 0.310 0.409 2.092 NaN NaN
elr winter 2019 0.993 0.067 0.220 0.364 1.336 0.480 2.645
baseline all 0.985 0.075 0.371 0.443 2.696 NaN NaN
elr all 0.990 0.085 0.318 0.416 2.796 0.491 3.184

Extended logistic regression plots

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