GMS location: 850

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.972 0.087 0.466 0.491 2.400 NaN NaN
forest winter 2016 0.983 0.043 0.401 0.449 2.375 0.444 1.654
baseline winter 2017 0.972 0.051 0.411 0.461 2.416 NaN NaN
forest winter 2017 0.963 0.000e+00 0.333 0.413 2.091 0.428 1.406
baseline winter 2018 0.993 0.035 0.478 0.519 2.210 NaN NaN
forest winter 2018 0.985 0.000e+00 0.395 0.474 2.049 0.433 1.290
baseline winter 2019 0.980 0.095 0.945 0.683 4.074 NaN NaN
forest winter 2019 0.980 0.143 0.835 0.606 3.604 0.403 1.546
baseline all 0.979 0.062 0.548 0.528 4.074 NaN NaN
forest all 0.979 0.036 0.467 0.477 3.604 0.430 1.481

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.972 0.087 0.466 0.491 2.400 NaN NaN
elr winter 2016 0.967 0.043 0.415 0.484 2.396 0.501 1.774
baseline winter 2017 0.972 0.051 0.411 0.461 2.416 NaN NaN
elr winter 2017 0.972 0.026 0.346 0.434 2.237 0.465 1.511
baseline winter 2018 0.993 0.035 0.478 0.519 2.210 NaN NaN
elr winter 2018 0.978 0.069 0.395 0.476 1.942 0.475 1.643
baseline winter 2019 0.980 0.095 0.945 0.683 4.074 NaN NaN
elr winter 2019 0.980 0.191 0.820 0.625 3.732 0.457 1.974
baseline all 0.979 0.062 0.548 0.528 4.074 NaN NaN
elr all 0.973 0.071 0.471 0.497 3.732 0.477 1.717

Extended logistic regression plots

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