GMS location: 1102

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.094 0.286 0.399 1.987 NaN NaN
forest winter 2016 0.975 0.062 0.288 0.378 2.029 0.455 4.255
baseline winter 2017 0.983 0.051 0.434 0.465 2.185 NaN NaN
forest winter 2017 0.983 0.051 0.324 0.403 1.998 0.464 5.315
baseline winter 2018 0.976 0.182 0.346 0.425 2.210 NaN NaN
forest winter 2018 0.984 0.151 0.331 0.420 2.184 0.466 4.447
baseline winter 2019 0.992 0.000e+00 0.247 0.359 1.639 NaN NaN
forest winter 2019 0.992 0.000e+00 0.172 0.296 1.466 0.454 3.725
baseline all 0.987 0.092 0.327 0.412 2.210 NaN NaN
forest all 0.983 0.075 0.281 0.375 2.184 0.460 4.432

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.094 0.286 0.399 1.987 NaN NaN
elr winter 2016 0.975 0.094 0.276 0.402 1.891 0.534 4.230
baseline winter 2017 0.983 0.051 0.434 0.465 2.185 NaN NaN
elr winter 2017 0.983 0.026 0.374 0.445 1.903 0.549 6.489
baseline winter 2018 0.976 0.182 0.346 0.425 2.210 NaN NaN
elr winter 2018 0.984 0.151 0.320 0.433 2.003 0.548 5.917
baseline winter 2019 0.992 0.000e+00 0.247 0.359 1.639 NaN NaN
elr winter 2019 1.000 0.000e+00 0.209 0.356 1.311 0.527 3.837
baseline all 0.987 0.092 0.327 0.412 2.210 NaN NaN
elr all 0.985 0.075 0.295 0.409 2.003 0.539 5.087

Extended logistic regression plots

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