From: Forest fire pattern and vulnerability mapping using deep learning in Nepal
Method | Fire year | Forest fire-vulnerability risk class (n) | FALSE (n) | TRUE (n) | Probability of detection | |||
---|---|---|---|---|---|---|---|---|
Very low | Low | High | Very high | |||||
MaxEnt | 2019 | 295 | 155 | 432 | 354 | 450 | 786 | 0.64 |
2018 | 251 | 156 | 432 | 357 | 407 | 789 | 0.66 | |
2017 | 325 | 172 | 515 | 329 | 497 | 844 | 0.63 | |
Overall | 871 | 483 | 1379 | 1040 | 1354 | 2419 | 0.64 | |
DNN | 2019 | 206 | 138 | 538 | 354 | 344 | 892 | 0.72 |
2018 | 171 | 134 | 525 | 366 | 305 | 891 | 0.74 | |
2017 | 252 | 175 | 551 | 363 | 427 | 914 | 0.68 | |
Overall | 629 | 447 | 1614 | 1083 | 1076 | 2697 | 0.71 |