GMS location: 832

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.982 0.167 0.355 0.391 2.399 NaN NaN
forest winter 2016 0.988 0.111 0.318 0.380 2.285 0.488 4.122
baseline winter 2017 0.991 0.077 0.356 0.425 2.731 NaN NaN
forest winter 2017 1.000 0.154 0.296 0.385 2.708 0.450 3.366
baseline winter 2018 0.976 0.143 0.300 0.418 1.970 NaN NaN
forest winter 2018 0.992 0.143 0.257 0.376 1.782 0.463 2.587
baseline winter 2019 0.986 0.000e+00 0.287 0.363 2.348 NaN NaN
forest winter 2019 0.993 0.125 0.205 0.312 1.996 0.454 2.679
baseline all 0.984 0.099 0.326 0.399 2.731 NaN NaN
forest all 0.993 0.139 0.271 0.364 2.708 0.465 3.235

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.982 0.167 0.355 0.391 2.399 NaN NaN
elr winter 2016 0.994 0.111 0.335 0.407 2.170 0.541 5.631
baseline winter 2017 0.991 0.077 0.356 0.425 2.731 NaN NaN
elr winter 2017 0.982 0.103 0.306 0.395 2.482 0.513 4.161
baseline winter 2018 0.976 0.143 0.300 0.418 1.970 NaN NaN
elr winter 2018 0.992 0.143 0.267 0.370 1.910 0.514 3.632
baseline winter 2019 0.986 0.000e+00 0.287 0.363 2.348 NaN NaN
elr winter 2019 0.993 0.125 0.229 0.348 1.944 0.477 3.205
baseline all 0.984 0.099 0.326 0.399 2.731 NaN NaN
elr all 0.991 0.119 0.287 0.382 2.482 0.513 4.232

Extended logistic regression plots

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