GMS location: 454

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.133 1.013 0.684 4.398 NaN NaN
forest winter 2016 0.994 0.133 0.916 0.630 4.091 0.498 2.864
baseline winter 2017 0.981 0.065 0.533 0.515 3.499 NaN NaN
forest winter 2017 0.981 0.043 0.508 0.513 3.472 0.499 1.675
baseline winter 2018 0.991 0.029 0.368 0.447 2.039 NaN NaN
forest winter 2018 0.983 0.000e+00 0.320 0.433 1.868 0.509 1.402
baseline winter 2019 0.992 0.062 0.322 0.394 2.206 NaN NaN
forest winter 2019 0.992 0.000e+00 0.277 0.384 1.711 0.500 1.421
baseline all 0.987 0.071 0.598 0.525 4.398 NaN NaN
forest all 0.989 0.048 0.541 0.502 4.091 0.501 1.929

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.133 1.013 0.684 4.398 NaN NaN
elr winter 2016 0.989 0.133 0.871 0.634 3.282 0.589 3.936
baseline winter 2017 0.981 0.065 0.533 0.515 3.499 NaN NaN
elr winter 2017 0.981 0.043 0.526 0.549 3.588 0.573 2.223
baseline winter 2018 0.991 0.029 0.368 0.447 2.039 NaN NaN
elr winter 2018 0.991 0.029 0.319 0.434 1.681 0.573 1.880
baseline winter 2019 0.992 0.062 0.322 0.394 2.206 NaN NaN
elr winter 2019 1.000 0.000e+00 0.295 0.390 1.777 0.526 1.709
baseline all 0.987 0.071 0.598 0.525 4.398 NaN NaN
elr all 0.991 0.056 0.535 0.514 3.588 0.567 2.566

Extended logistic regression plots

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