GMS location: 505

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.091 0.484 0.533 2.400 NaN NaN
forest winter 2016 0.994 0.136 0.383 0.459 2.281 0.470 4.382
baseline winter 2017 0.942 0.035 0.402 0.465 2.288 NaN NaN
forest winter 2017 0.950 0.069 0.257 0.369 1.645 0.469 2.416
baseline winter 2018 0.993 0.100 0.325 0.436 1.570 NaN NaN
forest winter 2018 0.971 0.133 0.246 0.378 1.564 0.463 2.259
baseline winter 2019 0.993 0.000e+00 0.311 0.428 1.800 NaN NaN
forest winter 2019 0.993 0.077 0.222 0.360 1.685 0.455 2.595
baseline all 0.983 0.064 0.385 0.469 2.400 NaN NaN
forest all 0.979 0.106 0.283 0.396 2.281 0.464 2.997

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 0.091 0.484 0.533 2.400 NaN NaN
elr winter 2016 0.983 0.091 0.423 0.508 2.310 0.541 6.951
baseline winter 2017 0.942 0.035 0.402 0.465 2.288 NaN NaN
elr winter 2017 0.942 0.069 0.314 0.413 1.929 0.510 3.930
baseline winter 2018 0.993 0.100 0.325 0.436 1.570 NaN NaN
elr winter 2018 0.979 0.133 0.296 0.420 1.608 0.523 4.214
baseline winter 2019 0.993 0.000e+00 0.311 0.428 1.800 NaN NaN
elr winter 2019 0.993 0.077 0.269 0.400 1.669 0.499 3.515
baseline all 0.983 0.064 0.385 0.469 2.400 NaN NaN
elr all 0.976 0.096 0.331 0.440 2.310 0.520 4.793

Extended logistic regression plots

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