GMS location: 610

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.067 0.350 0.444 1.822 NaN NaN
forest winter 2016 0.989 0.133 0.256 0.375 1.530 0.453 3.599
baseline winter 2017 0.966 0.000e+00 0.456 0.472 2.723 NaN NaN
forest winter 2017 0.992 0.000e+00 0.314 0.402 1.901 0.466 4.853
baseline winter 2018 0.986 0.062 0.400 0.451 2.315 NaN NaN
forest winter 2018 0.979 0.125 0.313 0.403 2.090 0.482 3.899
baseline winter 2019 1.000 0.071 0.309 0.411 2.043 NaN NaN
forest winter 2019 1.000 0.071 0.237 0.380 1.298 0.478 3.914
baseline all 0.984 0.045 0.377 0.445 2.723 NaN NaN
forest all 0.990 0.081 0.279 0.389 2.090 0.469 4.023

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.067 0.350 0.444 1.822 NaN NaN
elr winter 2016 0.983 0.067 0.269 0.419 1.654 0.546 3.657
baseline winter 2017 0.966 0.000e+00 0.456 0.472 2.723 NaN NaN
elr winter 2017 0.983 0.000e+00 0.343 0.429 2.278 0.514 3.947
baseline winter 2018 0.986 0.062 0.400 0.451 2.315 NaN NaN
elr winter 2018 0.993 0.094 0.314 0.433 1.982 0.549 4.379
baseline winter 2019 1.000 0.071 0.309 0.411 2.043 NaN NaN
elr winter 2019 1.000 0.071 0.327 0.447 1.486 0.534 3.666
baseline all 0.984 0.045 0.377 0.445 2.723 NaN NaN
elr all 0.990 0.054 0.310 0.431 2.278 0.537 3.908

Extended logistic regression plots

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