| Field |
Value |
Language |
|
dc.contributor.author |
Naderpour, M
https://orcid.org/0000-0002-3549-3875
|
|
|
dc.contributor.author |
Rizeei, HM |
|
|
dc.contributor.author |
Ramezani, F
https://orcid.org/0000-0002-0368-321X
|
|
|
dc.date.accessioned |
2021-07-01T04:26:45Z |
|
|
dc.date.available |
2021-07-01T04:26:45Z |
|
|
dc.identifier.citation |
Remote Sensing, 13, (13), pp. 2513-2513 |
|
|
dc.identifier.issn |
2072-4292 |
|
|
dc.identifier.uri |
http://hdl.handle.net/10453/149793
|
|
|
dc.description.abstract |
<jats:p>Forest fire is one of the foremost environmental disasters that threatens the Australian community. Recognition of the occurrence patterns of fires and the identification of fire risk is beneficial to mitigate probable fire threats. Machine learning techniques are recognized as well-known approaches to solving non-linearity problems such as forest fire risk. However, assessing such environmental multivariate disasters has always been challenging as modelling may be biased from multiple uncertainty sources such as the quality and quantity of input parameters, training processes, and a default setup for hyper-parameters. In this study, we propose a spatial framework to quantify the forest fire risk in the Northern Beaches area of Sydney. Thirty-six significant key indicators contributing to forest fire risk were selected and spatially mapped from different contexts such as topography, morphology, climate, human-induced, social, and physical perspectives as input to our model. Optimized deep neural networks were developed to maximize the capability of the multilayer perceptron for forest fire susceptibility assessment. The results show high precision of developed model against accuracy assessment metrics of ROC = 95.1%, PRC = 93.8%, and k coefficient = 94.3%. The proposed framework follows a stepwise procedure to run multiple scenarios to calculate the probability of forest risk with new input contributing parameters. This model improves adaptability and decision-making as it can be adapted to different regions of Australia with a minor localization adoption requirement of the weighting procedure.</jats:p> |
|
|
dc.language |
en |
|
|
dc.publisher |
MDPI AG |
|
|
dc.relation.ispartof |
Remote Sensing |
|
|
dc.relation.isbasedon |
10.3390/rs13132513 |
|
|
dc.rights |
info:eu-repo/semantics/openAccess |
|
|
dc.subject |
0203 Classical Physics, 0406 Physical Geography and Environmental Geoscience, 0909 Geomatic Engineering |
|
|
dc.title |
Forest Fire Risk Prediction: A Spatial Deep Neural Network-Based Framework |
|
|
dc.type |
Journal Article |
|
|
utslib.citation.volume |
13 |
|
|
utslib.for |
0203 Classical Physics |
|
|
utslib.for |
0406 Physical Geography and Environmental Geoscience |
|
|
utslib.for |
0909 Geomatic Engineering |
|
|
pubs.organisational-group |
/University of Technology Sydney |
|
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Engineering and Information Technology |
|
|
pubs.organisational-group |
/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute |
|
|
pubs.organisational-group |
/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems |
|
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling |
|
|
pubs.organisational-group |
/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science |
|
|
utslib.copyright.status |
open_access |
* |
|
dc.date.updated |
2021-07-01T04:26:37Z |
|
|
pubs.issue |
13 |
|
|
pubs.publication-status |
Published online |
|
|
pubs.volume |
13 |
|
|
utslib.citation.issue |
13 |
|
