GMS location: 1160

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 1.000 0.088 0.343 0.414 2.309 NaN NaN
forest winter 2016 1.000 0.088 0.314 0.417 1.966 0.560 3.075
baseline winter 2017 0.981 0.048 0.510 0.518 2.301 NaN NaN
forest winter 2017 0.971 0.024 0.413 0.467 2.079 0.541 3.554
baseline winter 2018 0.985 0.109 0.468 0.477 2.318 NaN NaN
forest winter 2018 0.978 0.109 0.392 0.468 1.950 0.555 3.163
baseline winter 2019 0.986 0.000e+00 0.394 0.411 2.099 NaN NaN
forest winter 2019 0.993 0.000e+00 0.304 0.394 1.879 0.557 3.491
baseline all 0.989 0.072 0.424 0.453 2.318 NaN NaN
forest all 0.987 0.065 0.354 0.436 2.079 0.554 3.299

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 1.000 0.088 0.343 0.414 2.309 NaN NaN
elr winter 2016 0.994 0.088 0.322 0.433 2.118 0.630 3.542
baseline winter 2017 0.981 0.048 0.510 0.518 2.301 NaN NaN
elr winter 2017 0.962 0.024 0.445 0.494 2.068 0.582 3.659
baseline winter 2018 0.985 0.109 0.468 0.477 2.318 NaN NaN
elr winter 2018 0.978 0.130 0.436 0.492 2.082 0.609 3.734
baseline winter 2019 0.986 0.000e+00 0.394 0.411 2.099 NaN NaN
elr winter 2019 0.986 0.000e+00 0.375 0.457 2.008 0.599 3.347
baseline all 0.989 0.072 0.424 0.453 2.318 NaN NaN
elr all 0.981 0.072 0.392 0.468 2.118 0.607 3.574

Extended logistic regression plots

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