GMS location: 1405

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.138 0.343 0.454 1.699 NaN NaN
forest winter 2016 0.983 0.138 0.260 0.382 1.403 0.477 4.114
baseline winter 2017 0.965 0.050 0.472 0.517 2.350 NaN NaN
forest winter 2017 0.965 0.025 0.403 0.475 1.864 0.479 3.698
baseline winter 2018 1.000 0.091 0.364 0.465 1.700 NaN NaN
forest winter 2018 0.992 0.061 0.277 0.410 1.601 0.480 2.776
baseline winter 2019 0.993 0.000e+00 0.382 0.441 2.208 NaN NaN
forest winter 2019 0.993 0.000e+00 0.279 0.392 1.815 0.488 3.645
baseline all 0.986 0.078 0.386 0.468 2.350 NaN NaN
forest all 0.984 0.061 0.300 0.412 1.864 0.481 3.595

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 0.138 0.343 0.454 1.699 NaN NaN
elr winter 2016 0.978 0.138 0.329 0.448 1.647 0.551 3.627
baseline winter 2017 0.965 0.050 0.472 0.517 2.350 NaN NaN
elr winter 2017 0.974 0.050 0.401 0.481 2.047 0.519 3.996
baseline winter 2018 1.000 0.091 0.364 0.465 1.700 NaN NaN
elr winter 2018 1.000 0.061 0.315 0.457 1.628 0.541 3.526
baseline winter 2019 0.993 0.000e+00 0.382 0.441 2.208 NaN NaN
elr winter 2019 0.993 0.000e+00 0.323 0.430 1.858 0.520 3.337
baseline all 0.986 0.078 0.386 0.468 2.350 NaN NaN
elr all 0.986 0.070 0.341 0.453 2.047 0.534 3.618

Extended logistic regression plots

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