GMS location: 716

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.974 0.059 0.333 0.435 1.916 NaN NaN
forest winter 2016 0.989 0.059 0.224 0.354 1.674 0.425 2.656
baseline winter 2017 0.946 0.088 0.429 0.493 2.668 NaN NaN
forest winter 2017 0.955 0.000e+00 0.267 0.392 1.843 0.444 3.622
baseline winter 2018 0.978 0.111 0.447 0.507 2.200 NaN NaN
forest winter 2018 0.993 0.111 0.378 0.444 2.320 0.429 3.759
baseline winter 2019 0.987 0.000e+00 0.265 0.386 2.004 NaN NaN
forest winter 2019 0.987 0.091 0.170 0.318 1.337 0.428 2.667
baseline all 0.973 0.079 0.364 0.453 2.668 NaN NaN
forest all 0.983 0.056 0.257 0.375 2.320 0.431 3.131

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.974 0.059 0.333 0.435 1.916 NaN NaN
elr winter 2016 0.984 0.059 0.269 0.388 1.681 0.486 3.849
baseline winter 2017 0.946 0.088 0.429 0.493 2.668 NaN NaN
elr winter 2017 0.955 0.059 0.308 0.425 2.230 0.468 4.039
baseline winter 2018 0.978 0.111 0.447 0.507 2.200 NaN NaN
elr winter 2018 0.993 0.185 0.414 0.477 2.721 0.482 5.085
baseline winter 2019 0.987 0.000e+00 0.265 0.386 2.004 NaN NaN
elr winter 2019 0.987 0.091 0.226 0.361 1.900 0.464 3.240
baseline all 0.973 0.079 0.364 0.453 2.668 NaN NaN
elr all 0.981 0.101 0.302 0.411 2.721 0.476 4.039

Extended logistic regression plots

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