GMS location: 914

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.973 0.048 0.335 0.429 2.399 NaN NaN
forest winter 2016 0.978 0.143 0.288 0.395 2.285 0.506 5.519
baseline winter 2017 0.971 0.024 0.375 0.421 2.504 NaN NaN
forest winter 2017 0.971 0.049 0.306 0.381 1.985 0.464 4.128
baseline winter 2018 0.979 0.118 0.291 0.398 2.100 NaN NaN
forest winter 2018 0.986 0.088 0.239 0.359 2.188 0.489 3.352
baseline winter 2019 0.986 0.000e+00 0.283 0.383 2.541 NaN NaN
forest winter 2019 0.986 0.000e+00 0.212 0.347 1.642 0.483 3.563
baseline all 0.977 0.054 0.320 0.409 2.541 NaN NaN
forest all 0.981 0.072 0.262 0.371 2.285 0.487 4.212

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.973 0.048 0.335 0.429 2.399 NaN NaN
elr winter 2016 0.978 0.000e+00 0.304 0.421 2.232 0.562 5.482
baseline winter 2017 0.971 0.024 0.375 0.421 2.504 NaN NaN
elr winter 2017 0.981 0.049 0.322 0.412 2.226 0.515 5.024
baseline winter 2018 0.979 0.118 0.291 0.398 2.100 NaN NaN
elr winter 2018 0.979 0.176 0.256 0.364 2.074 0.543 4.979
baseline winter 2019 0.986 0.000e+00 0.283 0.383 2.541 NaN NaN
elr winter 2019 0.993 0.000e+00 0.210 0.355 1.464 0.527 4.202
baseline all 0.977 0.054 0.320 0.409 2.541 NaN NaN
elr all 0.983 0.072 0.274 0.389 2.232 0.539 4.957

Extended logistic regression plots

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