GMS location: 529

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.053 0.305 0.414 1.980 NaN NaN
forest winter 2016 0.994 0.053 0.242 0.366 1.803 0.471 2.842
baseline winter 2017 0.976 0.069 0.367 0.445 1.998 NaN NaN
forest winter 2017 0.976 0.069 0.280 0.383 1.783 0.469 3.860
baseline winter 2018 0.986 0.045 0.405 0.435 3.191 NaN NaN
forest winter 2018 0.993 0.091 0.347 0.405 3.133 0.473 3.890
baseline winter 2019 0.986 0.000e+00 0.277 0.366 1.900 NaN NaN
forest winter 2019 0.993 0.000e+00 0.237 0.344 1.909 0.473 3.833
baseline all 0.986 0.049 0.337 0.415 3.191 NaN NaN
forest all 0.990 0.061 0.276 0.374 3.133 0.472 3.565

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 0.053 0.305 0.414 1.980 NaN NaN
elr winter 2016 0.989 0.000e+00 0.287 0.410 1.857 0.529 5.005
baseline winter 2017 0.976 0.069 0.367 0.445 1.998 NaN NaN
elr winter 2017 0.968 0.069 0.321 0.425 1.898 0.534 5.423
baseline winter 2018 0.986 0.045 0.405 0.435 3.191 NaN NaN
elr winter 2018 0.986 0.091 0.343 0.418 2.681 0.566 7.635
baseline winter 2019 0.986 0.000e+00 0.277 0.366 1.900 NaN NaN
elr winter 2019 0.993 0.000e+00 0.265 0.358 2.447 0.544 5.136
baseline all 0.986 0.049 0.337 0.415 3.191 NaN NaN
elr all 0.985 0.049 0.303 0.404 2.681 0.543 5.786

Extended logistic regression plots

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