GMS location: 1223

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.097 0.356 0.448 2.011 NaN NaN
forest winter 2016 0.994 0.065 0.282 0.383 1.952 0.462 2.521
baseline winter 2017 0.965 0.128 0.499 0.505 2.806 NaN NaN
forest winter 2017 0.982 0.077 0.380 0.444 2.295 0.472 3.476
baseline winter 2018 0.991 0.077 0.430 0.451 3.047 NaN NaN
forest winter 2018 1.000 0.115 0.392 0.438 2.777 0.466 2.469
baseline winter 2019 0.982 0.083 0.357 0.434 2.093 NaN NaN
forest winter 2019 0.982 0.083 0.310 0.413 1.842 0.453 2.304
baseline all 0.984 0.102 0.408 0.460 3.047 NaN NaN
forest all 0.990 0.083 0.336 0.416 2.777 0.464 2.702

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 0.097 0.356 0.448 2.011 NaN NaN
elr winter 2016 0.989 0.065 0.336 0.447 1.982 0.540 2.608
baseline winter 2017 0.965 0.128 0.499 0.505 2.806 NaN NaN
elr winter 2017 0.956 0.077 0.440 0.484 2.866 0.533 3.045
baseline winter 2018 0.991 0.077 0.430 0.451 3.047 NaN NaN
elr winter 2018 0.983 0.115 0.440 0.489 2.822 0.559 3.794
baseline winter 2019 0.982 0.083 0.357 0.434 2.093 NaN NaN
elr winter 2019 0.982 0.083 0.397 0.479 2.223 0.491 2.569
baseline all 0.984 0.102 0.408 0.460 3.047 NaN NaN
elr all 0.979 0.083 0.397 0.472 2.866 0.533 2.976

Extended logistic regression plots

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