GMS location: 810

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.036 0.448 0.474 3.072 NaN NaN
forest winter 2016 0.977 0.036 0.428 0.468 2.768 0.539 3.626
baseline winter 2017 0.991 0.100 0.296 0.409 1.754 NaN NaN
forest winter 2017 0.991 0.075 0.283 0.403 1.866 0.508 3.053
baseline winter 2018 0.987 0.129 0.440 0.476 1.977 NaN NaN
forest winter 2018 0.980 0.129 0.332 0.399 1.964 0.532 2.925
baseline winter 2019 0.985 0.120 0.443 0.470 2.592 NaN NaN
forest winter 2019 0.985 0.160 0.358 0.408 2.630 0.519 2.648
baseline all 0.986 0.097 0.411 0.460 3.072 NaN NaN
forest all 0.983 0.097 0.355 0.422 2.768 0.526 3.093

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 0.036 0.448 0.474 3.072 NaN NaN
elr winter 2016 0.977 0.036 0.481 0.516 2.792 0.657 5.860
baseline winter 2017 0.991 0.100 0.296 0.409 1.754 NaN NaN
elr winter 2017 0.991 0.100 0.283 0.405 1.611 0.570 3.328
baseline winter 2018 0.987 0.129 0.440 0.476 1.977 NaN NaN
elr winter 2018 0.973 0.129 0.351 0.448 1.942 0.618 4.091
baseline winter 2019 0.985 0.120 0.443 0.470 2.592 NaN NaN
elr winter 2019 0.993 0.120 0.336 0.409 2.315 0.575 3.475
baseline all 0.986 0.097 0.411 0.460 3.072 NaN NaN
elr all 0.983 0.097 0.370 0.449 2.792 0.609 4.295

Extended logistic regression plots

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