GMS location: 252

Random forest results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.167 0.338 0.445 2.155 NaN NaN
forest winter 2016 0.995 0.167 0.321 0.442 1.956 0.585 2.038
baseline winter 2017 0.957 0.028 0.465 0.480 2.410 NaN NaN
forest winter 2017 0.949 0.028 0.373 0.443 1.971 0.488 2.027
baseline winter 2018 0.993 0.081 1.205 0.528 1.168e+01 NaN NaN
forest winter 2018 0.993 0.027 1.105 0.475 1.133e+01 0.518 2.067
baseline winter 2019 0.991 0.105 1.332 0.576 1.007e+01 NaN NaN
forest winter 2019 0.991 0.105 1.446 0.596 1.002e+01 0.645 2.546
baseline all 0.984 0.082 0.796 0.502 1.168e+01 NaN NaN
forest all 0.984 0.064 0.767 0.482 1.133e+01 0.557 2.147

Random forest plots

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Extended logistic regression results

names period power significance meanSquareError absError maxError CRPS IGN
baseline winter 2016 0.989 0.167 0.338 0.445 2.155 NaN NaN
elr winter 2016 1.000 0.111 0.344 0.469 2.062 0.509 1.808
baseline winter 2017 0.957 0.028 0.465 0.480 2.410 NaN NaN
elr winter 2017 0.949 0.028 0.396 0.458 2.111 0.504 1.973
baseline winter 2018 0.993 0.081 1.205 0.528 1.168e+01 NaN NaN
elr winter 2018 0.993 0.108 1.120 0.506 1.128e+01 0.518 2.208
baseline winter 2019 0.991 0.105 1.332 0.576 1.007e+01 NaN NaN
elr winter 2019 0.991 0.158 1.242 0.579 9.837 0.545 2.952
baseline all 0.984 0.082 0.796 0.502 1.168e+01 NaN NaN
elr all 0.985 0.091 0.741 0.498 1.128e+01 0.518 2.184

Extended logistic regression plots

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