GMS location: 958

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.977 0.111 0.354 0.465 1.576 NaN NaN
forest winter 2016 0.983 0.111 0.269 0.398 1.734 0.477 6.319
baseline winter 2017 0.975 0.065 0.374 0.424 2.912 NaN NaN
forest winter 2017 0.967 0.032 0.255 0.334 2.955 0.474 4.229
baseline winter 2018 0.985 0.250 0.327 0.420 2.099 NaN NaN
forest winter 2018 0.985 0.250 0.271 0.369 2.051 0.483 3.642
baseline winter 2019 0.979 0.000e+00 0.379 0.445 2.181 NaN NaN
forest winter 2019 0.993 0.000e+00 0.228 0.360 1.546 0.451 3.054
baseline all 0.978 0.075 0.363 0.443 2.912 NaN NaN
forest all 0.982 0.060 0.254 0.367 2.955 0.470 4.548

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.977 0.111 0.354 0.465 1.576 NaN NaN
elr winter 2016 0.989 0.111 0.257 0.394 1.739 0.571 7.313
baseline winter 2017 0.975 0.065 0.374 0.424 2.912 NaN NaN
elr winter 2017 0.975 0.065 0.278 0.376 2.815 0.572 7.078
baseline winter 2018 0.985 0.250 0.327 0.420 2.099 NaN NaN
elr winter 2018 0.970 0.250 0.326 0.431 2.020 0.608 8.162
baseline winter 2019 0.979 0.000e+00 0.379 0.445 2.181 NaN NaN
elr winter 2019 0.979 0.000e+00 0.218 0.373 1.376 0.541 5.891
baseline all 0.978 0.075 0.363 0.443 2.912 NaN NaN
elr all 0.980 0.075 0.261 0.388 2.815 0.568 6.969

Extended logistic regression plots

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