A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area

Tien Bui, D; Bui, QT; Nguyen, QP; Pradhan, B; Nampak, H; Trinh, PT

A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area

Tien Bui, D Bui, QT Nguyen, QP Pradhan, B

Nampak, H Trinh, PT

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Publication Type:: Journal Article
Citation:: Agricultural and Forest Meteorology, 2017, 233 pp. 32 - 44
Issue Date:: 2017-02-15

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Field	Value	Language
dc.contributor.author	Tien Bui, D	en_US
dc.contributor.author	Bui, QT	en_US
dc.contributor.author	Nguyen, QP	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.contributor.author	Nampak, H	en_US
dc.contributor.author	Trinh, PT	en_US
dc.date.issued	2017-02-15	en_US
dc.identifier.citation	Agricultural and Forest Meteorology, 2017, 233 pp. 32 - 44	en_US
dc.identifier.issn	0168-1923	en_US
dc.identifier.uri	http://hdl.handle.net/10453/119802
dc.description.abstract	© 2016 Elsevier B.V. This paper proposes and validates a novel hybrid artificial intelligent approach, named as Particle Swarm Optimized Neural Fuzzy (PSO-NF), for spatial modeling of tropical forest fire susceptibility. In the proposed approach, a Neural Fuzzy inference system (NF) was used to establish the forest fire model whereas Particle Swarm Optimization (PSO) was adopted to investigate the best values for the model parameters. Tropical forest at the province of Lam Dong (Central Highland of Vietnam) was used as a case study. For this purpose, historic forest fires and ten ignition factors (slope, aspect, elevation, land use, Normalized Difference Vegetation Index, distance to road, distance to residence area, temperature, wind speed, and rainfall) were collected from various sources to construct a GIS database, and then, the database was used to develop and validate the proposed model. The performance of the forest model was assessed using the Receiver Operating Characteristic curve, area under the curve (AUC), and several statistical measures. The results showed that the proposed model performs well, both on the training dataset (AUC = 0.932) and the validation dataset (AUC = 0.916). The usability of the proposed model was further assessed through comparisons with those derived from two benchmark state-of-the art machine learning methods, Random Forests (RF) and Support Vector Machine (SVM). Because the performance of the proposed model is better than the two benchmark models, we concluded that the PSO-NF model is a valid alternative tool that should be considered for tropical forest fire susceptibility modeling. The result in this study is useful for forest planning and management in forest fire prone areas.	en_US
dc.relation.ispartof	Agricultural and Forest Meteorology	en_US
dc.relation.isbasedon	10.1016/j.agrformet.2016.11.002	en_US
dc.subject.classification	Meteorology & Atmospheric Sciences	en_US
dc.title	A hybrid artificial intelligence approach using GIS-based neural-fuzzy inference system and particle swarm optimization for forest fire susceptibility modeling at a tropical area	en_US
dc.type	Journal Article
utslib.citation.volume	233	en_US
utslib.for	0406 Physical Geography and Environmental Geoscience	en_US
utslib.for	0701 Agriculture, Land and Farm Management	en_US
utslib.for	0699 Other Biological Sciences	en_US
utslib.for	04 Earth Sciences	en_US
utslib.for	06 Biological Sciences	en_US
utslib.for	07 Agricultural and Veterinary Sciences	en_US
pubs.embargo.period	Not known	en_US
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/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	233	en_US

Abstract:

© 2016 Elsevier B.V. This paper proposes and validates a novel hybrid artificial intelligent approach, named as Particle Swarm Optimized Neural Fuzzy (PSO-NF), for spatial modeling of tropical forest fire susceptibility. In the proposed approach, a Neural Fuzzy inference system (NF) was used to establish the forest fire model whereas Particle Swarm Optimization (PSO) was adopted to investigate the best values for the model parameters. Tropical forest at the province of Lam Dong (Central Highland of Vietnam) was used as a case study. For this purpose, historic forest fires and ten ignition factors (slope, aspect, elevation, land use, Normalized Difference Vegetation Index, distance to road, distance to residence area, temperature, wind speed, and rainfall) were collected from various sources to construct a GIS database, and then, the database was used to develop and validate the proposed model. The performance of the forest model was assessed using the Receiver Operating Characteristic curve, area under the curve (AUC), and several statistical measures. The results showed that the proposed model performs well, both on the training dataset (AUC = 0.932) and the validation dataset (AUC = 0.916). The usability of the proposed model was further assessed through comparisons with those derived from two benchmark state-of-the art machine learning methods, Random Forests (RF) and Support Vector Machine (SVM). Because the performance of the proposed model is better than the two benchmark models, we concluded that the PSO-NF model is a valid alternative tool that should be considered for tropical forest fire susceptibility modeling. The result in this study is useful for forest planning and management in forest fire prone areas.

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http://hdl.handle.net/10453/119802