GMS location: 608

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.069 0.391 0.439 2.301 NaN NaN
forest winter 2016 0.983 0.035 0.305 0.400 2.149 0.552 2.907
baseline winter 2017 0.973 0.095 0.578 0.541 3.444 NaN NaN
forest winter 2017 0.973 0.095 0.444 0.464 2.526 0.535 3.219
baseline winter 2018 0.986 0.000e+00 0.491 0.491 2.909 NaN NaN
forest winter 2018 0.986 0.000e+00 0.402 0.455 2.758 0.561 3.035
baseline winter 2019 0.986 0.083 0.263 0.368 1.863 NaN NaN
forest winter 2019 0.993 0.083 0.183 0.328 1.282 0.561 2.663
baseline all 0.986 0.060 0.429 0.459 3.444 NaN NaN
forest all 0.984 0.051 0.334 0.412 2.758 0.553 2.954

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 0.069 0.391 0.439 2.301 NaN NaN
elr winter 2016 0.977 0.035 0.338 0.456 1.946 0.651 4.345
baseline winter 2017 0.973 0.095 0.578 0.541 3.444 NaN NaN
elr winter 2017 0.973 0.095 0.479 0.502 2.966 0.596 4.361
baseline winter 2018 0.986 0.000e+00 0.491 0.491 2.909 NaN NaN
elr winter 2018 0.979 0.000e+00 0.414 0.482 2.622 0.652 4.993
baseline winter 2019 0.986 0.083 0.263 0.368 1.863 NaN NaN
elr winter 2019 0.986 0.083 0.250 0.404 1.373 0.594 3.140
baseline all 0.986 0.060 0.429 0.459 3.444 NaN NaN
elr all 0.979 0.051 0.369 0.461 2.966 0.626 4.244

Extended logistic regression plots

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