GMS location: 715

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.995 0.000e+00 0.346 0.427 2.996 NaN NaN
forest winter 2016 0.995 0.111 0.259 0.364 2.904 0.446 3.598
baseline winter 2017 0.968 0.071 0.378 0.478 1.904 NaN NaN
forest winter 2017 0.976 0.107 0.226 0.357 1.308 0.439 4.541
baseline winter 2018 0.986 0.125 0.362 0.464 2.020 NaN NaN
forest winter 2018 0.993 0.167 0.264 0.384 2.067 0.441 3.272
baseline winter 2019 0.980 0.000e+00 0.335 0.400 2.361 NaN NaN
forest winter 2019 0.980 0.000e+00 0.230 0.347 1.769 0.437 3.216
baseline all 0.983 0.061 0.354 0.441 2.996 NaN NaN
forest all 0.987 0.110 0.246 0.363 2.904 0.441 3.640

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.995 0.000e+00 0.346 0.427 2.996 NaN NaN
elr winter 2016 0.995 0.000e+00 0.313 0.412 2.825 0.517 4.757
baseline winter 2017 0.968 0.071 0.378 0.478 1.904 NaN NaN
elr winter 2017 0.976 0.107 0.440 0.447 4.418 0.517 4.616
baseline winter 2018 0.986 0.125 0.362 0.464 2.020 NaN NaN
elr winter 2018 0.986 0.208 0.286 0.396 2.293 0.493 4.283
baseline winter 2019 0.980 0.000e+00 0.335 0.400 2.361 NaN NaN
elr winter 2019 0.980 0.000e+00 0.256 0.375 1.518 0.480 4.136
baseline all 0.983 0.061 0.354 0.441 2.996 NaN NaN
elr all 0.985 0.098 0.321 0.407 4.418 0.502 4.465

Extended logistic regression plots

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