GMS location: 912

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.979 0.062 0.328 0.431 2.402 NaN NaN
forest winter 2016 0.995 0.062 0.267 0.389 2.616 0.478 3.311
baseline winter 2017 0.975 0.088 0.358 0.449 2.254 NaN NaN
forest winter 2017 0.983 0.176 0.278 0.387 1.943 0.470 3.591
baseline winter 2018 0.993 0.094 0.412 0.450 3.268 NaN NaN
forest winter 2018 0.993 0.125 0.362 0.417 3.288 0.462 3.155
baseline winter 2019 0.960 0.000e+00 0.371 0.447 2.518 NaN NaN
forest winter 2019 0.960 0.000e+00 0.254 0.377 1.947 0.451 2.609
baseline all 0.977 0.076 0.366 0.444 3.268 NaN NaN
forest all 0.984 0.120 0.291 0.393 3.288 0.466 3.171

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.979 0.062 0.328 0.431 2.402 NaN NaN
elr winter 2016 0.990 0.062 0.283 0.399 2.631 0.561 4.299
baseline winter 2017 0.975 0.088 0.358 0.449 2.254 NaN NaN
elr winter 2017 0.983 0.088 0.273 0.389 1.842 0.538 3.827
baseline winter 2018 0.993 0.094 0.412 0.450 3.268 NaN NaN
elr winter 2018 1.000 0.125 0.376 0.431 3.229 0.512 4.292
baseline winter 2019 0.960 0.000e+00 0.371 0.447 2.518 NaN NaN
elr winter 2019 0.974 0.000e+00 0.284 0.430 1.576 0.513 4.007
baseline all 0.977 0.076 0.366 0.444 3.268 NaN NaN
elr all 0.987 0.087 0.305 0.412 3.229 0.532 4.128

Extended logistic regression plots

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