GMS location: 561

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 1.000 0.222 0.416 0.495 2.137 NaN NaN
forest winter 2016 1.000 0.222 0.369 0.445 2.173 0.491 3.333
baseline winter 2017 0.991 0.024 0.351 0.439 2.092 NaN NaN
forest winter 2017 0.982 0.024 0.328 0.432 1.849 0.508 3.237
baseline winter 2018 0.993 0.031 0.282 0.393 2.090 NaN NaN
forest winter 2018 0.993 0.062 0.249 0.369 2.300 0.515 2.510
baseline winter 2019 1.000 0.000e+00 0.304 0.383 2.699 NaN NaN
forest winter 2019 1.000 0.000e+00 0.288 0.383 2.355 0.534 3.079
baseline all 0.996 0.073 0.343 0.432 2.699 NaN NaN
forest all 0.995 0.082 0.311 0.409 2.355 0.510 3.043

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 1.000 0.222 0.416 0.495 2.137 NaN NaN
elr winter 2016 0.994 0.148 0.402 0.484 2.413 0.550 5.267
baseline winter 2017 0.991 0.024 0.351 0.439 2.092 NaN NaN
elr winter 2017 0.991 0.024 0.340 0.440 1.956 0.553 4.609
baseline winter 2018 0.993 0.031 0.282 0.393 2.090 NaN NaN
elr winter 2018 0.993 0.031 0.304 0.414 2.755 0.564 4.409
baseline winter 2019 1.000 0.000e+00 0.304 0.383 2.699 NaN NaN
elr winter 2019 1.000 0.000e+00 0.318 0.405 2.510 0.570 4.757
baseline all 0.996 0.073 0.343 0.432 2.699 NaN NaN
elr all 0.995 0.054 0.345 0.439 2.755 0.559 4.786

Extended logistic regression plots

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