GMS location: 1406

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.978 0.154 0.292 0.427 1.630 NaN NaN
forest winter 2016 0.994 0.192 0.234 0.375 1.346 0.442 3.696
baseline winter 2017 0.982 0.026 0.417 0.475 3.000 NaN NaN
forest winter 2017 0.991 0.051 0.302 0.405 2.034 0.453 5.107
baseline winter 2018 0.992 0.097 0.394 0.481 2.085 NaN NaN
forest winter 2018 0.992 0.129 0.315 0.436 1.692 0.445 3.587
baseline winter 2019 0.985 0.077 0.384 0.439 2.318 NaN NaN
forest winter 2019 0.985 0.077 0.283 0.375 2.122 0.450 3.969
baseline all 0.984 0.083 0.365 0.453 3.000 NaN NaN
forest all 0.991 0.110 0.280 0.396 2.122 0.447 4.058

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.978 0.154 0.292 0.427 1.630 NaN NaN
elr winter 2016 0.994 0.115 0.265 0.418 1.443 0.516 3.945
baseline winter 2017 0.982 0.026 0.417 0.475 3.000 NaN NaN
elr winter 2017 0.982 0.103 0.323 0.421 2.378 0.531 4.501
baseline winter 2018 0.992 0.097 0.394 0.481 2.085 NaN NaN
elr winter 2018 0.984 0.065 0.333 0.452 2.369 0.525 4.338
baseline winter 2019 0.985 0.077 0.384 0.439 2.318 NaN NaN
elr winter 2019 0.992 0.077 0.269 0.391 1.827 0.505 3.995
baseline all 0.984 0.083 0.365 0.453 3.000 NaN NaN
elr all 0.989 0.092 0.295 0.421 2.378 0.519 4.177

Extended logistic regression plots

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