GMS location: 523

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.045 0.400 0.462 2.501 NaN NaN
forest winter 2016 1.000 0.045 0.312 0.381 2.583 0.458 3.672
baseline winter 2017 0.960 0.071 0.350 0.449 2.482 NaN NaN
forest winter 2017 0.968 0.071 0.229 0.363 1.975 0.446 2.457
baseline winter 2018 1.000 0.037 0.365 0.437 2.635 NaN NaN
forest winter 2018 0.991 0.037 0.314 0.397 2.743 0.455 2.564
baseline winter 2019 0.983 0.000e+00 0.291 0.417 1.739 NaN NaN
forest winter 2019 0.983 0.000e+00 0.260 0.373 2.082 0.418 2.409
baseline all 0.985 0.049 0.364 0.447 2.635 NaN NaN
forest all 0.987 0.049 0.283 0.379 2.743 0.449 2.899

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.045 0.400 0.462 2.501 NaN NaN
elr winter 2016 0.988 0.045 0.328 0.403 2.444 0.531 5.528
baseline winter 2017 0.960 0.071 0.350 0.449 2.482 NaN NaN
elr winter 2017 0.968 0.107 0.247 0.377 2.007 0.492 3.492
baseline winter 2018 1.000 0.037 0.365 0.437 2.635 NaN NaN
elr winter 2018 0.991 0.037 0.347 0.423 2.889 0.521 4.938
baseline winter 2019 0.983 0.000e+00 0.291 0.417 1.739 NaN NaN
elr winter 2019 0.983 0.000e+00 0.293 0.410 2.148 0.482 3.523
baseline all 0.985 0.049 0.364 0.447 2.635 NaN NaN
elr all 0.983 0.061 0.306 0.402 2.889 0.512 4.572

Extended logistic regression plots

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