GMS location: 1222

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.988 0.000e+00 0.340 0.429 2.047 NaN NaN
forest winter 2016 0.976 0.000e+00 0.258 0.367 1.832 0.525 3.190
baseline winter 2017 0.982 0.075 0.536 0.514 2.448 NaN NaN
forest winter 2017 0.974 0.075 0.397 0.452 2.194 0.520 4.329
baseline winter 2018 0.992 0.077 0.399 0.462 2.383 NaN NaN
forest winter 2018 0.992 0.077 0.343 0.437 1.926 0.497 2.846
baseline winter 2019 0.985 0.000e+00 0.288 0.389 1.671 NaN NaN
forest winter 2019 0.985 0.000e+00 0.223 0.349 1.451 0.499 2.999
baseline all 0.987 0.045 0.389 0.448 2.448 NaN NaN
forest all 0.981 0.045 0.303 0.399 2.194 0.512 3.344

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.988 0.000e+00 0.340 0.429 2.047 NaN NaN
elr winter 2016 0.963 0.000e+00 0.339 0.462 2.004 0.607 3.900
baseline winter 2017 0.982 0.075 0.536 0.514 2.448 NaN NaN
elr winter 2017 0.974 0.075 0.490 0.495 2.433 0.553 4.622
baseline winter 2018 0.992 0.077 0.399 0.462 2.383 NaN NaN
elr winter 2018 0.992 0.077 0.380 0.474 2.052 0.600 4.442
baseline winter 2019 0.985 0.000e+00 0.288 0.389 1.671 NaN NaN
elr winter 2019 0.985 0.000e+00 0.301 0.421 1.444 0.537 3.023
baseline all 0.987 0.045 0.389 0.448 2.448 NaN NaN
elr all 0.977 0.045 0.376 0.463 2.433 0.577 3.997

Extended logistic regression plots

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