GMS location: 951

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.979 0.053 0.323 0.422 1.768 NaN NaN
forest winter 2016 0.989 0.210 0.241 0.379 1.533 0.425 2.998
baseline winter 2017 0.976 0.071 0.369 0.461 1.870 NaN NaN
forest winter 2017 0.976 0.107 0.248 0.378 1.412 0.439 4.277
baseline winter 2018 0.974 0.179 0.461 0.493 3.142 NaN NaN
forest winter 2018 0.968 0.179 0.363 0.428 3.069 0.433 3.187
baseline winter 2019 0.981 0.000e+00 0.393 0.481 2.146 NaN NaN
forest winter 2019 0.974 0.077 0.266 0.391 1.808 0.426 2.676
baseline all 0.978 0.091 0.385 0.463 3.142 NaN NaN
forest all 0.978 0.148 0.280 0.394 3.069 0.430 3.246

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.979 0.053 0.323 0.422 1.768 NaN NaN
elr winter 2016 0.995 0.158 0.261 0.404 1.886 0.491 3.356
baseline winter 2017 0.976 0.071 0.369 0.461 1.870 NaN NaN
elr winter 2017 0.976 0.107 0.318 0.434 1.786 0.483 3.261
baseline winter 2018 0.974 0.179 0.461 0.493 3.142 NaN NaN
elr winter 2018 0.981 0.143 0.382 0.455 2.973 0.489 3.743
baseline winter 2019 0.981 0.000e+00 0.393 0.481 2.146 NaN NaN
elr winter 2019 0.974 0.077 0.322 0.445 1.775 0.479 3.423
baseline all 0.978 0.091 0.385 0.463 3.142 NaN NaN
elr all 0.982 0.125 0.318 0.433 2.973 0.485 3.451

Extended logistic regression plots

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