GMS location: 213

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.167 0.299 0.397 1.737 NaN NaN
forest winter 2016 0.994 0.167 0.257 0.377 1.770 0.509 3.687
baseline winter 2017 0.963 0.048 0.438 0.494 2.171 NaN NaN
forest winter 2017 0.963 0.024 0.370 0.452 1.953 0.516 3.809
baseline winter 2018 0.993 0.111 0.407 0.479 2.121 NaN NaN
forest winter 2018 0.986 0.056 0.368 0.454 2.370 0.547 3.820
baseline winter 2019 0.984 0.000e+00 0.341 0.469 1.360 NaN NaN
forest winter 2019 1.000 0.000e+00 0.277 0.401 1.231 0.492 3.424
baseline all 0.984 0.093 0.370 0.454 2.171 NaN NaN
forest all 0.986 0.068 0.320 0.421 2.370 0.520 3.724

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 0.167 0.299 0.397 1.737 NaN NaN
elr winter 2016 0.994 0.233 0.290 0.407 1.739 0.572 3.522
baseline winter 2017 0.963 0.048 0.438 0.494 2.171 NaN NaN
elr winter 2017 0.963 0.048 0.388 0.473 2.168 0.575 4.629
baseline winter 2018 0.993 0.111 0.407 0.479 2.121 NaN NaN
elr winter 2018 0.979 0.056 0.387 0.480 2.416 0.598 4.810
baseline winter 2019 0.984 0.000e+00 0.341 0.469 1.360 NaN NaN
elr winter 2019 0.984 0.000e+00 0.287 0.414 1.367 0.530 3.389
baseline all 0.984 0.093 0.370 0.454 2.171 NaN NaN
elr all 0.982 0.093 0.342 0.445 2.416 0.575 4.154

Extended logistic regression plots

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