GMS location: 957

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.000e+00 0.287 0.417 1.790 NaN NaN
forest winter 2016 0.989 0.105 0.219 0.360 1.502 0.462 4.771
baseline winter 2017 0.967 0.030 0.406 0.427 2.953 NaN NaN
forest winter 2017 0.975 0.030 0.285 0.364 2.954 0.471 5.563
baseline winter 2018 0.987 0.069 0.332 0.417 2.299 NaN NaN
forest winter 2018 0.987 0.069 0.273 0.383 2.330 0.464 4.933
baseline winter 2019 0.992 0.067 0.261 0.370 1.822 NaN NaN
forest winter 2019 0.992 0.067 0.193 0.325 1.538 0.462 4.227
baseline all 0.985 0.042 0.320 0.409 2.953 NaN NaN
forest all 0.986 0.062 0.243 0.360 2.954 0.464 4.878

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 0.000e+00 0.287 0.417 1.790 NaN NaN
elr winter 2016 0.984 0.000e+00 0.221 0.357 1.735 0.578 7.817
baseline winter 2017 0.967 0.030 0.406 0.427 2.953 NaN NaN
elr winter 2017 0.967 0.061 0.332 0.418 2.918 0.555 8.384
baseline winter 2018 0.987 0.069 0.332 0.417 2.299 NaN NaN
elr winter 2018 0.987 0.035 0.275 0.376 2.354 0.559 7.664
baseline winter 2019 0.992 0.067 0.261 0.370 1.822 NaN NaN
elr winter 2019 0.992 0.067 0.208 0.357 1.518 0.555 6.388
baseline all 0.985 0.042 0.320 0.409 2.953 NaN NaN
elr all 0.983 0.042 0.257 0.376 2.918 0.563 7.606

Extended logistic regression plots

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