GMS location: 817

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.978 0.235 0.719 0.586 4.400 NaN NaN
forest winter 2016 0.995 0.118 0.680 0.567 4.509 0.422 2.554
baseline winter 2017 0.974 0.000e+00 0.280 0.366 1.877 NaN NaN
forest winter 2017 0.974 0.000e+00 0.252 0.366 1.808 0.426 1.501
baseline winter 2018 0.987 0.000e+00 0.404 0.460 2.215 NaN NaN
forest winter 2018 1.000 0.042 0.338 0.436 1.926 0.407 1.453
baseline winter 2019 0.965 0.000e+00 0.407 0.454 2.217 NaN NaN
forest winter 2019 0.986 0.000e+00 0.308 0.401 1.851 0.399 1.366
baseline all 0.977 0.046 0.471 0.476 4.400 NaN NaN
forest all 0.990 0.035 0.414 0.452 4.509 0.414 1.768

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.978 0.235 0.719 0.586 4.400 NaN NaN
elr winter 2016 0.989 0.176 0.680 0.560 4.157 0.472 2.993
baseline winter 2017 0.974 0.000e+00 0.280 0.366 1.877 NaN NaN
elr winter 2017 0.966 0.000e+00 0.248 0.373 1.550 0.482 1.820
baseline winter 2018 0.987 0.000e+00 0.404 0.460 2.215 NaN NaN
elr winter 2018 0.994 0.000e+00 0.330 0.441 1.921 0.448 1.732
baseline winter 2019 0.965 0.000e+00 0.407 0.454 2.217 NaN NaN
elr winter 2019 0.993 0.000e+00 0.312 0.413 1.905 0.421 1.457
baseline all 0.977 0.046 0.471 0.476 4.400 NaN NaN
elr all 0.987 0.035 0.412 0.456 4.157 0.456 2.059

Extended logistic regression plots

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