GMS location: 1158

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.067 0.343 0.436 1.950 NaN NaN
forest winter 2016 0.994 0.067 0.283 0.398 1.686 0.511 3.296
baseline winter 2017 0.975 0.029 0.403 0.465 2.037 NaN NaN
forest winter 2017 0.975 0.029 0.356 0.449 1.969 0.530 3.897
baseline winter 2018 0.982 0.083 0.395 0.445 2.030 NaN NaN
forest winter 2018 0.982 0.125 0.384 0.447 1.881 0.540 3.134
baseline winter 2019 1.000 0.000e+00 0.347 0.460 2.014 NaN NaN
forest winter 2019 1.000 0.000e+00 0.253 0.375 1.788 0.536 4.448
baseline all 0.987 0.053 0.372 0.449 2.037 NaN NaN
forest all 0.987 0.064 0.323 0.420 1.969 0.526 3.575

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 0.067 0.343 0.436 1.950 NaN NaN
elr winter 2016 0.983 0.033 0.360 0.461 1.947 0.610 4.292
baseline winter 2017 0.975 0.029 0.403 0.465 2.037 NaN NaN
elr winter 2017 0.966 0.029 0.423 0.494 2.301 0.598 4.546
baseline winter 2018 0.982 0.083 0.395 0.445 2.030 NaN NaN
elr winter 2018 0.982 0.125 0.390 0.464 1.771 0.584 4.311
baseline winter 2019 1.000 0.000e+00 0.347 0.460 2.014 NaN NaN
elr winter 2019 0.986 0.000e+00 0.390 0.479 2.359 0.551 3.734
baseline all 0.987 0.053 0.372 0.449 2.037 NaN NaN
elr all 0.979 0.053 0.388 0.473 2.359 0.592 4.288

Extended logistic regression plots

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