GMS location: 901

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.973 0.053 0.332 0.443 1.712 NaN NaN
forest winter 2016 0.978 0.105 0.234 0.370 1.752 0.464 5.218
baseline winter 2017 0.950 0.121 0.369 0.441 2.654 NaN NaN
forest winter 2017 0.950 0.151 0.254 0.368 2.253 0.467 3.988
baseline winter 2018 0.973 0.103 0.315 0.419 2.553 NaN NaN
forest winter 2018 0.973 0.172 0.259 0.377 2.243 0.472 4.041
baseline winter 2019 0.987 0.077 0.324 0.432 1.939 NaN NaN
forest winter 2019 0.993 0.154 0.208 0.343 1.521 0.465 4.069
baseline all 0.972 0.096 0.334 0.434 2.654 NaN NaN
forest all 0.975 0.149 0.239 0.365 2.253 0.467 4.377

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.973 0.053 0.332 0.443 1.712 NaN NaN
elr winter 2016 0.973 0.105 0.265 0.402 1.902 0.558 7.440
baseline winter 2017 0.950 0.121 0.369 0.441 2.654 NaN NaN
elr winter 2017 0.967 0.121 0.249 0.365 2.110 0.526 6.113
baseline winter 2018 0.973 0.103 0.315 0.419 2.553 NaN NaN
elr winter 2018 0.980 0.207 0.305 0.428 2.183 0.553 7.743
baseline winter 2019 0.987 0.077 0.324 0.432 1.939 NaN NaN
elr winter 2019 0.993 0.154 0.212 0.356 1.329 0.505 5.516
baseline all 0.972 0.096 0.334 0.434 2.654 NaN NaN
elr all 0.978 0.149 0.260 0.390 2.183 0.537 6.778

Extended logistic regression plots

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