GMS location: 852

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.981 0.045 0.567 0.549 2.906 NaN NaN
forest winter 2016 0.981 0.068 0.544 0.529 2.599 0.460 1.439
baseline winter 2017 0.970 0.113 0.872 0.664 3.465 NaN NaN
forest winter 2017 0.980 0.094 0.677 0.615 3.022 0.446 1.438
baseline winter 2018 0.962 0.128 1.647 0.808 7.548 NaN NaN
forest winter 2018 0.985 0.154 1.525 0.764 7.459 0.437 1.533
baseline winter 2019 0.972 0.029 0.573 0.491 3.942 NaN NaN
forest winter 2019 0.963 0.057 0.426 0.460 2.708 0.505 1.410
baseline all 0.972 0.082 0.914 0.629 7.548 NaN NaN
forest all 0.978 0.094 0.800 0.594 7.459 0.461 1.456

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.981 0.045 0.567 0.549 2.906 NaN NaN
elr winter 2016 0.987 0.023 0.575 0.561 2.775 0.477 1.256
baseline winter 2017 0.970 0.113 0.872 0.664 3.465 NaN NaN
elr winter 2017 0.990 0.057 0.696 0.649 3.082 0.502 1.357
baseline winter 2018 0.962 0.128 1.647 0.808 7.548 NaN NaN
elr winter 2018 0.985 0.154 1.617 0.782 7.440 0.512 1.826
baseline winter 2019 0.972 0.029 0.573 0.491 3.942 NaN NaN
elr winter 2019 0.972 0.086 0.541 0.497 3.561 0.432 1.135
baseline all 0.972 0.082 0.914 0.629 7.548 NaN NaN
elr all 0.984 0.076 0.861 0.624 7.440 0.482 1.399

Extended logistic regression plots

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