GMS location: 911

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.984 0.000e+00 0.329 0.431 1.993 NaN NaN
forest winter 2016 0.995 0.056 0.269 0.392 1.718 0.477 3.822
baseline winter 2017 0.957 0.026 0.386 0.457 2.667 NaN NaN
forest winter 2017 0.948 0.026 0.316 0.410 1.939 0.464 3.643
baseline winter 2018 0.992 0.069 0.349 0.441 2.481 NaN NaN
forest winter 2018 0.976 0.069 0.318 0.422 2.089 0.472 3.158
baseline winter 2019 0.986 0.000e+00 0.340 0.407 2.739 NaN NaN
forest winter 2019 0.978 0.000e+00 0.252 0.346 2.664 0.463 2.875
baseline all 0.981 0.029 0.349 0.434 2.739 NaN NaN
forest all 0.977 0.039 0.287 0.392 2.664 0.470 3.406

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.984 0.000e+00 0.329 0.431 1.993 NaN NaN
elr winter 2016 0.995 0.111 0.299 0.422 1.650 0.537 5.228
baseline winter 2017 0.957 0.026 0.386 0.457 2.667 NaN NaN
elr winter 2017 0.948 0.026 0.312 0.427 2.119 0.540 4.931
baseline winter 2018 0.992 0.069 0.349 0.441 2.481 NaN NaN
elr winter 2018 0.984 0.069 0.317 0.417 2.169 0.523 4.679
baseline winter 2019 0.986 0.000e+00 0.340 0.407 2.739 NaN NaN
elr winter 2019 0.986 0.000e+00 0.258 0.371 2.497 0.502 4.064
baseline all 0.981 0.029 0.349 0.434 2.739 NaN NaN
elr all 0.981 0.049 0.296 0.410 2.497 0.526 4.760

Extended logistic regression plots

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