GMS location: 362

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 1.000 0.053 0.326 0.442 1.702 NaN NaN
forest winter 2016 1.000 0.053 0.246 0.377 1.610 0.446 4.978
baseline winter 2017 0.957 0.088 0.391 0.458 2.189 NaN NaN
forest winter 2017 0.957 0.118 0.272 0.390 1.560 0.474 5.242
baseline winter 2018 1.000 0.172 0.299 0.405 1.871 NaN NaN
forest winter 2018 0.978 0.138 0.242 0.361 1.683 0.463 3.981
baseline winter 2019 1.000 0.000e+00 0.331 0.442 1.884 NaN NaN
forest winter 2019 1.000 0.000e+00 0.248 0.376 1.336 0.438 3.457
baseline all 0.990 0.094 0.338 0.439 2.189 NaN NaN
forest all 0.986 0.094 0.252 0.377 1.683 0.455 4.510

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 1.000 0.053 0.326 0.442 1.702 NaN NaN
elr winter 2016 0.995 0.053 0.294 0.432 1.786 0.548 5.589
baseline winter 2017 0.957 0.088 0.391 0.458 2.189 NaN NaN
elr winter 2017 0.957 0.088 0.307 0.423 1.751 0.546 6.252
baseline winter 2018 1.000 0.172 0.299 0.405 1.871 NaN NaN
elr winter 2018 0.989 0.103 0.236 0.384 1.814 0.526 4.724
baseline winter 2019 1.000 0.000e+00 0.331 0.442 1.884 NaN NaN
elr winter 2019 1.000 0.000e+00 0.267 0.399 1.406 0.483 3.919
baseline all 0.990 0.094 0.338 0.439 2.189 NaN NaN
elr all 0.986 0.073 0.280 0.413 1.814 0.529 5.211

Extended logistic regression plots

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