GMS location: 251

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.994 0.250 0.423 0.496 2.001 NaN NaN
forest winter 2016 0.994 0.107 0.357 0.458 1.928 0.608 3.374
baseline winter 2017 1.000 0.119 0.486 0.493 2.205 NaN NaN
forest winter 2017 0.973 0.071 0.394 0.458 2.022 0.582 3.374
baseline winter 2018 1.000 0.088 0.466 0.497 2.152 NaN NaN
forest winter 2018 0.986 0.029 0.334 0.444 1.646 0.613 3.027
baseline winter 2019 0.994 0.000e+00 0.386 0.463 2.441 NaN NaN
forest winter 2019 0.987 0.000e+00 0.312 0.408 1.893 0.607 3.291
baseline all 0.997 0.129 0.439 0.488 2.441 NaN NaN
forest all 0.986 0.060 0.348 0.443 2.022 0.603 3.267

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 0.250 0.423 0.496 2.001 NaN NaN
elr winter 2016 0.989 0.143 0.385 0.496 1.925 0.669 5.146
baseline winter 2017 1.000 0.119 0.486 0.493 2.205 NaN NaN
elr winter 2017 1.000 0.095 0.428 0.477 2.093 0.607 4.019
baseline winter 2018 1.000 0.088 0.466 0.497 2.152 NaN NaN
elr winter 2018 1.000 0.059 0.384 0.476 1.947 0.644 4.495
baseline winter 2019 0.994 0.000e+00 0.386 0.463 2.441 NaN NaN
elr winter 2019 0.994 0.000e+00 0.344 0.447 1.959 0.614 3.707
baseline all 0.997 0.129 0.439 0.488 2.441 NaN NaN
elr all 0.995 0.086 0.385 0.475 2.093 0.636 4.397

Extended logistic regression plots

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