GMS location: 851

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.965 0.143 0.448 0.494 2.591 NaN NaN
forest winter 2016 0.971 0.191 0.416 0.454 2.851 0.455 1.332
baseline winter 2017 0.983 0.081 0.591 0.526 3.139 NaN NaN
forest winter 2017 0.991 0.081 0.522 0.499 3.086 0.430 1.290
baseline winter 2018 0.978 0.179 0.769 0.587 4.442 NaN NaN
forest winter 2018 0.971 0.179 0.858 0.601 4.850 0.492 1.408
baseline winter 2019 0.985 0.045 1.083 0.608 5.369 NaN NaN
forest winter 2019 0.993 0.091 0.951 0.537 5.673 0.418 1.258
baseline all 0.977 0.118 0.712 0.552 5.369 NaN NaN
forest all 0.980 0.135 0.680 0.522 5.673 0.451 1.325

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.965 0.143 0.448 0.494 2.591 NaN NaN
elr winter 2016 0.977 0.095 0.449 0.498 3.034 0.470 1.285
baseline winter 2017 0.983 0.081 0.591 0.526 3.139 NaN NaN
elr winter 2017 0.974 0.108 0.527 0.512 3.311 0.478 1.349
baseline winter 2018 0.978 0.179 0.769 0.587 4.442 NaN NaN
elr winter 2018 0.986 0.205 0.725 0.543 5.018 0.443 1.297
baseline winter 2019 0.985 0.045 1.083 0.608 5.369 NaN NaN
elr winter 2019 0.993 0.091 1.021 0.560 5.868 0.424 1.254
baseline all 0.977 0.118 0.712 0.552 5.369 NaN NaN
elr all 0.982 0.135 0.672 0.527 5.868 0.454 1.295

Extended logistic regression plots

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