GMS location: 452

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.993 0.048 0.311 0.418 1.796 NaN NaN
forest winter 2016 0.993 0.048 0.245 0.362 1.554 0.504 3.094
baseline winter 2017 0.973 0.025 0.453 0.497 2.318 NaN NaN
forest winter 2017 0.964 0.000e+00 0.378 0.453 1.819 0.502 3.983
baseline winter 2018 0.993 0.125 0.378 0.426 2.630 NaN NaN
forest winter 2018 1.000 0.125 0.365 0.427 2.744 0.513 3.678
baseline winter 2019 0.985 0.000e+00 0.290 0.401 2.509 NaN NaN
forest winter 2019 0.985 0.000e+00 0.236 0.374 1.773 0.496 2.771
baseline all 0.987 0.059 0.358 0.435 2.630 NaN NaN
forest all 0.987 0.050 0.308 0.405 2.744 0.504 3.390

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.993 0.048 0.311 0.418 1.796 NaN NaN
elr winter 2016 0.986 0.048 0.327 0.447 1.788 0.586 4.500
baseline winter 2017 0.973 0.025 0.453 0.497 2.318 NaN NaN
elr winter 2017 0.955 0.025 0.386 0.473 1.984 0.582 4.670
baseline winter 2018 0.993 0.125 0.378 0.426 2.630 NaN NaN
elr winter 2018 1.000 0.125 0.371 0.442 2.663 0.536 3.852
baseline winter 2019 0.985 0.000e+00 0.290 0.401 2.509 NaN NaN
elr winter 2019 0.985 0.000e+00 0.283 0.420 1.903 0.539 3.519
baseline all 0.987 0.059 0.358 0.435 2.630 NaN NaN
elr all 0.983 0.059 0.343 0.446 2.663 0.560 4.128

Extended logistic regression plots

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