GMS location: 559

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.979 0.000e+00 0.523 0.555 2.154 NaN NaN
forest winter 2016 1.000 0.000e+00 0.288 0.405 2.275 0.384 1.551
baseline winter 2017 0.959 0.067 0.685 0.643 2.306 NaN NaN
forest winter 2017 0.976 0.100 0.358 0.466 1.654 0.400 2.004
baseline winter 2018 0.986 0.160 0.564 0.553 2.684 NaN NaN
forest winter 2018 0.993 0.200 0.382 0.437 2.637 0.391 1.563
baseline winter 2019 0.991 0.000e+00 0.644 0.607 2.487 NaN NaN
forest winter 2019 0.991 0.083 0.325 0.427 1.892 0.404 1.862
baseline all 0.979 0.076 0.595 0.585 2.684 NaN NaN
forest all 0.991 0.114 0.336 0.432 2.637 0.393 1.720

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.979 0.000e+00 0.523 0.555 2.154 NaN NaN
elr winter 2016 1.000 0.000e+00 0.295 0.429 1.963 0.490 2.438
baseline winter 2017 0.959 0.067 0.685 0.643 2.306 NaN NaN
elr winter 2017 0.984 0.033 0.364 0.476 1.696 0.495 2.913
baseline winter 2018 0.986 0.160 0.564 0.553 2.684 NaN NaN
elr winter 2018 0.993 0.160 0.378 0.440 2.953 0.490 2.868
baseline winter 2019 0.991 0.000e+00 0.644 0.607 2.487 NaN NaN
elr winter 2019 1.000 0.083 0.298 0.415 1.793 0.514 2.766
baseline all 0.979 0.076 0.595 0.585 2.684 NaN NaN
elr all 0.995 0.076 0.334 0.440 2.953 0.496 2.725

Extended logistic regression plots

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