GMS location: 107

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.978 0.154 0.441 0.475 2.691 NaN NaN
forest winter 2016 0.983 0.115 0.347 0.402 2.570 0.464 4.734
baseline winter 2017 0.970 0.061 0.545 0.505 3.358 NaN NaN
forest winter 2017 0.980 0.061 0.343 0.415 2.380 0.458 4.088
baseline winter 2018 0.984 0.091 0.407 0.456 2.418 NaN NaN
forest winter 2018 0.984 0.061 0.302 0.398 1.997 0.455 3.902
baseline winter 2019 1.000 0.000e+00 0.266 0.374 1.823 NaN NaN
forest winter 2019 1.000 0.000e+00 0.219 0.347 1.839 0.463 3.078
baseline all 0.984 0.086 0.408 0.450 3.358 NaN NaN
forest all 0.988 0.067 0.302 0.389 2.570 0.460 3.977

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.978 0.154 0.441 0.475 2.691 NaN NaN
elr winter 2016 0.989 0.154 0.355 0.452 2.415 0.569 4.004
baseline winter 2017 0.970 0.061 0.545 0.505 3.358 NaN NaN
elr winter 2017 0.970 0.091 0.372 0.433 2.659 0.501 3.474
baseline winter 2018 0.984 0.091 0.407 0.456 2.418 NaN NaN
elr winter 2018 0.992 0.121 0.313 0.430 1.880 0.549 4.068
baseline winter 2019 1.000 0.000e+00 0.266 0.374 1.823 NaN NaN
elr winter 2019 1.000 0.000e+00 0.250 0.403 1.490 0.526 3.152
baseline all 0.984 0.086 0.408 0.450 3.358 NaN NaN
elr all 0.989 0.106 0.321 0.430 2.659 0.539 3.692

Extended logistic regression plots

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