GMS location: 907

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.071 0.363 0.454 1.725 NaN NaN
forest winter 2016 0.994 0.286 0.243 0.365 1.803 0.445 5.194
baseline winter 2017 0.944 0.069 0.461 0.495 2.588 NaN NaN
forest winter 2017 0.984 0.069 0.262 0.369 2.227 0.439 3.776
baseline winter 2018 0.964 0.133 0.348 0.447 1.956 NaN NaN
forest winter 2018 0.978 0.167 0.259 0.385 2.095 0.432 3.632
baseline winter 2019 0.993 0.000e+00 0.403 0.460 2.175 NaN NaN
forest winter 2019 1.000 0.000e+00 0.207 0.348 1.237 0.427 3.519
baseline all 0.972 0.085 0.391 0.463 2.588 NaN NaN
forest all 0.990 0.134 0.244 0.367 2.227 0.436 4.084

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 0.071 0.363 0.454 1.725 NaN NaN
elr winter 2016 0.983 0.143 0.271 0.414 1.678 0.528 4.890
baseline winter 2017 0.944 0.069 0.461 0.495 2.588 NaN NaN
elr winter 2017 0.976 0.069 0.336 0.417 2.329 0.487 4.736
baseline winter 2018 0.964 0.133 0.348 0.447 1.956 NaN NaN
elr winter 2018 0.971 0.200 0.274 0.406 1.802 0.507 4.651
baseline winter 2019 0.993 0.000e+00 0.403 0.460 2.175 NaN NaN
elr winter 2019 1.000 0.000e+00 0.231 0.371 1.455 0.506 4.346
baseline all 0.972 0.085 0.391 0.463 2.588 NaN NaN
elr all 0.983 0.122 0.278 0.403 2.329 0.508 4.671

Extended logistic regression plots

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