GMS location: 379

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.240 0.484 0.536 2.273 NaN NaN
forest winter 2016 0.989 0.280 0.467 0.527 2.111 0.460 1.991
baseline winter 2017 0.982 0.022 0.590 0.537 4.599 NaN NaN
forest winter 2017 0.972 0.022 0.512 0.492 4.576 0.506 2.411
baseline winter 2018 0.981 0.081 0.458 0.485 2.411 NaN NaN
forest winter 2018 0.991 0.108 0.417 0.470 2.463 0.480 1.743
baseline winter 2019 0.978 0.125 0.637 0.515 5.484 NaN NaN
forest winter 2019 0.978 0.125 0.552 0.489 5.037 0.476 1.809
baseline all 0.981 0.098 0.539 0.520 5.484 NaN NaN
forest all 0.983 0.114 0.486 0.498 5.037 0.479 1.994

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.240 0.484 0.536 2.273 NaN NaN
elr winter 2016 0.995 0.280 0.435 0.533 1.837 0.556 2.463
baseline winter 2017 0.982 0.022 0.590 0.537 4.599 NaN NaN
elr winter 2017 0.972 0.022 0.548 0.521 4.587 0.616 3.034
baseline winter 2018 0.981 0.081 0.458 0.485 2.411 NaN NaN
elr winter 2018 0.991 0.081 0.383 0.458 2.308 0.565 2.478
baseline winter 2019 0.978 0.125 0.637 0.515 5.484 NaN NaN
elr winter 2019 0.985 0.125 0.525 0.475 5.190 0.529 2.515
baseline all 0.981 0.098 0.539 0.520 5.484 NaN NaN
elr all 0.987 0.106 0.471 0.500 5.190 0.566 2.612

Extended logistic regression plots

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