GMS location: 1162

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.097 0.370 0.439 2.197 NaN NaN
forest winter 2016 0.994 0.065 0.287 0.381 1.931 0.488 2.744
baseline winter 2017 0.992 0.028 0.460 0.490 2.130 NaN NaN
forest winter 2017 1.000 0.028 0.289 0.399 1.593 0.482 2.978
baseline winter 2018 0.972 0.151 0.449 0.477 2.669 NaN NaN
forest winter 2018 0.986 0.151 0.454 0.468 2.863 0.489 3.274
baseline winter 2019 0.993 0.000e+00 0.323 0.436 1.576 NaN NaN
forest winter 2019 1.000 0.000e+00 0.234 0.376 2.021 0.470 2.905
baseline all 0.986 0.082 0.400 0.459 2.669 NaN NaN
forest all 0.995 0.073 0.318 0.406 2.863 0.483 2.967

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 0.097 0.370 0.439 2.197 NaN NaN
elr winter 2016 0.977 0.065 0.316 0.426 2.007 0.554 3.677
baseline winter 2017 0.992 0.028 0.460 0.490 2.130 NaN NaN
elr winter 2017 0.992 0.028 0.345 0.438 1.781 0.520 3.481
baseline winter 2018 0.972 0.151 0.449 0.477 2.669 NaN NaN
elr winter 2018 0.986 0.151 0.475 0.501 3.146 0.569 4.881
baseline winter 2019 0.993 0.000e+00 0.323 0.436 1.576 NaN NaN
elr winter 2019 1.000 0.000e+00 0.331 0.452 2.358 0.518 3.157
baseline all 0.986 0.082 0.400 0.459 2.669 NaN NaN
elr all 0.988 0.073 0.366 0.454 3.146 0.542 3.824

Extended logistic regression plots

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