GMS location: 528
Random forest results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
1.000 |
0.059 |
0.264 |
0.407 |
1.599 |
NaN |
NaN |
forest |
winter 2016 |
1.000 |
0.118 |
0.244 |
0.389 |
1.269 |
0.478 |
2.386 |
baseline |
winter 2017 |
0.953 |
0.115 |
0.442 |
0.491 |
2.269 |
NaN |
NaN |
forest |
winter 2017 |
0.969 |
0.038 |
0.340 |
0.424 |
1.798 |
0.496 |
3.234 |
baseline |
winter 2018 |
0.985 |
0.038 |
0.443 |
0.485 |
2.865 |
NaN |
NaN |
forest |
winter 2018 |
0.985 |
0.115 |
0.356 |
0.427 |
2.861 |
0.496 |
3.508 |
baseline |
winter 2019 |
0.980 |
0.143 |
0.281 |
0.377 |
2.322 |
NaN |
NaN |
forest |
winter 2019 |
0.986 |
0.286 |
0.244 |
0.349 |
2.444 |
0.477 |
2.842 |
baseline |
all |
0.977 |
0.079 |
0.369 |
0.445 |
2.865 |
NaN |
NaN |
forest |
all |
0.984 |
0.105 |
0.303 |
0.399 |
2.861 |
0.488 |
3.068 |
Random forest plots
Extended logistic regression results
names |
period |
power |
significance |
meanSquareError |
absError |
maxError |
CRPS |
IGN |
baseline |
winter 2016 |
1.000 |
0.059 |
0.264 |
0.407 |
1.599 |
NaN |
NaN |
elr |
winter 2016 |
1.000 |
0.059 |
0.224 |
0.366 |
1.293 |
0.554 |
3.999 |
baseline |
winter 2017 |
0.953 |
0.115 |
0.442 |
0.491 |
2.269 |
NaN |
NaN |
elr |
winter 2017 |
0.969 |
0.038 |
0.377 |
0.462 |
2.032 |
0.535 |
4.961 |
baseline |
winter 2018 |
0.985 |
0.038 |
0.443 |
0.485 |
2.865 |
NaN |
NaN |
elr |
winter 2018 |
0.985 |
0.077 |
0.384 |
0.450 |
3.300 |
0.577 |
5.434 |
baseline |
winter 2019 |
0.980 |
0.143 |
0.281 |
0.377 |
2.322 |
NaN |
NaN |
elr |
winter 2019 |
0.980 |
0.143 |
0.260 |
0.375 |
2.469 |
0.555 |
4.098 |
baseline |
all |
0.977 |
0.079 |
0.369 |
0.445 |
2.865 |
NaN |
NaN |
elr |
all |
0.981 |
0.066 |
0.322 |
0.419 |
3.300 |
0.556 |
4.706 |
Extended logistic regression plots