GMS location: 815

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.975 0.105 0.480 0.504 3.099 NaN NaN
forest winter 2016 0.992 0.053 0.425 0.461 2.896 0.464 3.401
baseline winter 2017 0.983 0.135 0.362 0.435 2.365 NaN NaN
forest winter 2017 0.974 0.135 0.316 0.393 2.811 0.466 2.595
baseline winter 2018 0.987 0.040 0.387 0.451 2.156 NaN NaN
forest winter 2018 0.987 0.080 0.332 0.412 2.179 0.460 2.189
baseline winter 2019 0.986 0.056 0.314 0.400 2.096 NaN NaN
forest winter 2019 0.986 0.111 0.255 0.354 2.376 0.437 1.824
baseline all 0.983 0.091 0.383 0.446 3.099 NaN NaN
forest all 0.985 0.101 0.329 0.404 2.896 0.456 2.466

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.975 0.105 0.480 0.504 3.099 NaN NaN
elr winter 2016 0.983 0.053 0.422 0.467 2.971 0.522 3.726
baseline winter 2017 0.983 0.135 0.362 0.435 2.365 NaN NaN
elr winter 2017 0.974 0.135 0.309 0.408 2.726 0.545 3.441
baseline winter 2018 0.987 0.040 0.387 0.451 2.156 NaN NaN
elr winter 2018 0.987 0.080 0.332 0.443 2.181 0.519 3.436
baseline winter 2019 0.986 0.056 0.314 0.400 2.096 NaN NaN
elr winter 2019 0.986 0.111 0.269 0.382 2.046 0.493 2.659
baseline all 0.983 0.091 0.383 0.446 3.099 NaN NaN
elr all 0.983 0.101 0.330 0.424 2.971 0.520 3.305

Extended logistic regression plots

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