An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping
  • Metaadatok
Tartalom: http://hdl.handle.net/20.500.12944/20640
Archívum: Közszolgálati Tudásportál
Gyűjtemény: Publikációk
Karokon kívüli egységek
Cím:
An integrated GIS-based multivariate adaptive regression splines-cat swarm optimization for improving the accuracy of wildfire susceptibility mapping
Létrehozó:
Tao, Hai
Theruvil Sayed, Biju
Majdi, Ali
Zhou, Jincheng
Sagban, Rafid
Band, Shahab S.
Mosavi, Amir
Téma:
Wildfire susceptibility
geospatial
machine learning
cat swarm optimization
artificial intelligence
natural hazard
Műszaki tudományok
Bio-, környezet- és vegyészmérnöki tudományok
Tartalmi leírás:
A hybrid machine learning method is proposed for wildfire susceptibility mapping. For modeling a geographical information system (GIS) database including 11 influencing factors and 262 fire locations from 2013 to 2018 is used for developing an integrated multivariate adaptive regression splines (MARS). The cat swarm optimization (CSO) algorithm tunes the parameters of the MARS in order to generate accurate susceptibility maps. From the Pearson correlation results, it is observed that land use, temperature, and slope angle have strong correlation with the fire severity. The results demonstrate that the prediction capability of the MARS-CSO model outperforms model tree, reduced error pruning tree and MARS. The resulting wildfire risk map using MARS-CSO reveals that 20% of the study areas is categorized in the very low wildfire risk class, whereas 40% is under the very high class of fire hazard.
Típus:
info:eu-repo/semantics/other
Formátum:
application/pdf
Azonosító:
nke:1752-0762
nke:1010-6049
nke:10.1080/10106049.2023.2167005
nke:33591116
nke:10058509
nke:Geocarto International
nke:38
nke:1
nke:000922483200001
nke:85147182880
nke:GEOCAR INT
Létrehozó:
info:eu-repo/semantics/openAccess