GMS location: 961

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.984 0.136 0.319 0.421 1.538 NaN NaN
forest winter 2016 0.984 0.136 0.278 0.404 1.690 0.506 4.023
baseline winter 2017 0.972 0.135 0.367 0.426 2.045 NaN NaN
forest winter 2017 0.972 0.054 0.297 0.380 1.872 0.492 3.508
baseline winter 2018 0.992 0.074 0.348 0.453 2.303 NaN NaN
forest winter 2018 0.992 0.074 0.313 0.424 1.928 0.491 2.633
baseline winter 2019 0.983 0.056 0.449 0.480 2.561 NaN NaN
forest winter 2019 0.983 0.056 0.379 0.442 2.395 0.495 3.507
baseline all 0.983 0.106 0.364 0.443 2.561 NaN NaN
forest all 0.983 0.077 0.312 0.411 2.395 0.497 3.466

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.984 0.136 0.319 0.421 1.538 NaN NaN
elr winter 2016 0.984 0.136 0.291 0.417 1.522 0.572 4.136
baseline winter 2017 0.972 0.135 0.367 0.426 2.045 NaN NaN
elr winter 2017 0.972 0.054 0.297 0.405 1.626 0.558 3.982
baseline winter 2018 0.992 0.074 0.348 0.453 2.303 NaN NaN
elr winter 2018 0.992 0.074 0.342 0.452 2.070 0.539 3.987
baseline winter 2019 0.983 0.056 0.449 0.480 2.561 NaN NaN
elr winter 2019 0.983 0.056 0.334 0.425 2.289 0.543 3.908
baseline all 0.983 0.106 0.364 0.443 2.561 NaN NaN
elr all 0.983 0.077 0.314 0.425 2.289 0.555 4.017

Extended logistic regression plots

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