GMS location: 952

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.983 0.222 0.383 0.470 2.505 NaN NaN
forest winter 2016 0.989 0.222 0.316 0.417 2.532 0.436 3.123
baseline winter 2017 0.967 0.031 0.345 0.412 2.509 NaN NaN
forest winter 2017 0.967 0.062 0.254 0.374 2.593 0.449 2.617
baseline winter 2018 0.986 0.032 0.397 0.451 3.618 NaN NaN
forest winter 2018 0.986 0.032 0.350 0.422 3.445 0.442 3.238
baseline winter 2019 0.971 0.154 0.374 0.470 2.312 NaN NaN
forest winter 2019 0.978 0.154 0.264 0.395 1.551 0.414 1.827
baseline all 0.978 0.085 0.376 0.452 3.618 NaN NaN
forest all 0.981 0.096 0.299 0.404 3.445 0.436 2.751

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 0.222 0.383 0.470 2.505 NaN NaN
elr winter 2016 0.978 0.167 0.360 0.452 2.578 0.499 3.626
baseline winter 2017 0.967 0.031 0.345 0.412 2.509 NaN NaN
elr winter 2017 0.967 0.062 0.243 0.355 2.523 0.502 3.415
baseline winter 2018 0.986 0.032 0.397 0.451 3.618 NaN NaN
elr winter 2018 0.993 0.065 0.350 0.413 3.556 0.502 4.217
baseline winter 2019 0.971 0.154 0.374 0.470 2.312 NaN NaN
elr winter 2019 0.986 0.154 0.266 0.391 1.736 0.466 3.036
baseline all 0.978 0.085 0.376 0.452 3.618 NaN NaN
elr all 0.981 0.096 0.310 0.406 3.556 0.493 3.602

Extended logistic regression plots

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