GMS location: 921

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.045 0.356 0.444 2.314 NaN NaN
forest winter 2016 0.994 0.045 0.287 0.411 2.407 0.489 3.338
baseline winter 2017 0.964 0.050 0.420 0.478 2.841 NaN NaN
forest winter 2017 0.955 0.050 0.326 0.418 1.972 0.460 3.011
baseline winter 2018 0.986 0.105 0.396 0.470 2.146 NaN NaN
forest winter 2018 0.986 0.105 0.328 0.432 1.993 0.485 2.744
baseline winter 2019 0.972 0.000e+00 0.327 0.410 2.191 NaN NaN
forest winter 2019 0.972 0.000e+00 0.240 0.358 1.596 0.470 2.578
baseline all 0.979 0.062 0.374 0.451 2.841 NaN NaN
forest all 0.979 0.062 0.296 0.406 2.407 0.477 2.936

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.045 0.356 0.444 2.314 NaN NaN
elr winter 2016 1.000 0.045 0.304 0.434 2.267 0.553 4.266
baseline winter 2017 0.964 0.050 0.420 0.478 2.841 NaN NaN
elr winter 2017 0.955 0.075 0.326 0.425 2.098 0.507 3.429
baseline winter 2018 0.986 0.105 0.396 0.470 2.146 NaN NaN
elr winter 2018 0.993 0.105 0.355 0.435 2.239 0.507 3.504
baseline winter 2019 0.972 0.000e+00 0.327 0.410 2.191 NaN NaN
elr winter 2019 0.986 0.000e+00 0.260 0.377 1.517 0.492 2.806
baseline all 0.979 0.062 0.374 0.451 2.841 NaN NaN
elr all 0.986 0.071 0.312 0.419 2.267 0.517 3.548

Extended logistic regression plots

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