GMS location: 1232

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.987 0.094 0.420 0.469 2.545 NaN NaN
forest winter 2016 0.987 0.062 0.344 0.430 2.436 0.558 2.922
baseline winter 2017 0.975 0.059 0.535 0.538 2.063 NaN NaN
forest winter 2017 0.966 0.059 0.443 0.494 2.060 0.542 3.603
baseline winter 2018 0.986 0.171 0.421 0.480 2.611 NaN NaN
forest winter 2018 0.978 0.171 0.359 0.436 2.598 0.556 2.485
baseline winter 2019 0.987 0.000e+00 0.432 0.496 2.211 NaN NaN
forest winter 2019 0.987 0.000e+00 0.314 0.413 1.800 0.572 2.888
baseline all 0.984 0.097 0.449 0.494 2.611 NaN NaN
forest all 0.981 0.088 0.363 0.442 2.598 0.557 2.955

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.987 0.094 0.420 0.469 2.545 NaN NaN
elr winter 2016 0.987 0.094 0.375 0.490 2.328 0.667 4.275
baseline winter 2017 0.975 0.059 0.535 0.538 2.063 NaN NaN
elr winter 2017 0.950 0.029 0.496 0.546 2.015 0.629 3.697
baseline winter 2018 0.986 0.171 0.421 0.480 2.611 NaN NaN
elr winter 2018 0.986 0.114 0.378 0.453 2.474 0.654 3.777
baseline winter 2019 0.987 0.000e+00 0.432 0.496 2.211 NaN NaN
elr winter 2019 0.993 0.000e+00 0.385 0.462 1.833 0.596 3.227
baseline all 0.984 0.097 0.449 0.494 2.611 NaN NaN
elr all 0.981 0.071 0.405 0.486 2.474 0.638 3.766

Extended logistic regression plots

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