GMS location: 908

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.976 0.053 0.397 0.446 2.798 NaN NaN
forest winter 2016 0.988 0.158 0.339 0.402 2.572 0.499 5.653
baseline winter 2017 0.963 0.075 0.445 0.451 2.700 NaN NaN
forest winter 2017 0.982 0.025 0.348 0.391 2.354 0.478 4.403
baseline winter 2018 0.977 0.125 0.299 0.394 2.260 NaN NaN
forest winter 2018 0.962 0.062 0.245 0.369 1.900 0.488 3.727
baseline winter 2019 0.992 0.000e+00 0.315 0.402 2.173 NaN NaN
forest winter 2019 0.992 0.048 0.224 0.341 1.536 0.475 3.136
baseline all 0.978 0.071 0.364 0.424 2.798 NaN NaN
forest all 0.981 0.062 0.291 0.377 2.572 0.486 4.302

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.976 0.053 0.397 0.446 2.798 NaN NaN
elr winter 2016 0.976 0.105 0.337 0.408 2.531 0.560 4.165
baseline winter 2017 0.963 0.075 0.445 0.451 2.700 NaN NaN
elr winter 2017 0.982 0.050 0.374 0.418 2.467 0.524 3.783
baseline winter 2018 0.977 0.125 0.299 0.394 2.260 NaN NaN
elr winter 2018 0.985 0.062 0.286 0.411 2.249 0.545 3.687
baseline winter 2019 0.992 0.000e+00 0.315 0.402 2.173 NaN NaN
elr winter 2019 0.992 0.000e+00 0.255 0.374 1.788 0.533 3.161
baseline all 0.978 0.071 0.364 0.424 2.798 NaN NaN
elr all 0.983 0.054 0.314 0.403 2.531 0.542 3.725

Extended logistic regression plots

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