GMS location: 216

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.000e+00 0.328 0.440 1.680 NaN NaN
forest winter 2016 1.000 0.150 0.235 0.371 1.781 0.466 6.184
baseline winter 2017 0.966 0.059 0.413 0.481 2.020 NaN NaN
forest winter 2017 0.958 0.029 0.269 0.402 1.524 0.497 7.812
baseline winter 2018 0.993 0.061 0.296 0.414 1.720 NaN NaN
forest winter 2018 0.987 0.121 0.244 0.376 1.431 0.482 4.165
baseline winter 2019 0.993 0.071 0.288 0.404 1.801 NaN NaN
forest winter 2019 0.993 0.143 0.223 0.346 1.608 0.470 4.949
baseline all 0.987 0.050 0.329 0.434 2.020 NaN NaN
forest all 0.987 0.099 0.242 0.373 1.781 0.478 5.727

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.000e+00 0.328 0.440 1.680 NaN NaN
elr winter 2016 0.995 0.100 0.261 0.404 1.673 0.571 6.286
baseline winter 2017 0.966 0.059 0.413 0.481 2.020 NaN NaN
elr winter 2017 0.975 0.059 0.300 0.426 1.677 0.536 5.390
baseline winter 2018 0.993 0.061 0.296 0.414 1.720 NaN NaN
elr winter 2018 0.980 0.091 0.281 0.407 1.723 0.547 5.984
baseline winter 2019 0.993 0.071 0.288 0.404 1.801 NaN NaN
elr winter 2019 0.993 0.071 0.244 0.365 1.713 0.540 5.528
baseline all 0.987 0.050 0.329 0.434 2.020 NaN NaN
elr all 0.987 0.079 0.271 0.400 1.723 0.550 5.838

Extended logistic regression plots

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