Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS

Mojaddadi, H; Pradhan, B; Nampak, H; Ahmad, N; Ghazali, AHB

Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS

Mojaddadi, H

Pradhan, B

Nampak, H Ahmad, N Ghazali, AHB

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Publication Type:: Journal Article
Citation:: Geomatics, Natural Hazards and Risk, 2017, 8 (2), pp. 1080 - 1102
Issue Date:: 2017-12-15

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Field	Value	Language
dc.contributor.author	Mojaddadi, H https://orcid.org/0000-0002-7690-8440	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	Ahmad, N	en_US
dc.contributor.author	Ghazali, AHB	en_US
dc.date.issued	2017-12-15	en_US
dc.identifier.citation	Geomatics, Natural Hazards and Risk, 2017, 8 (2), pp. 1080 - 1102	en_US
dc.identifier.issn	1947-5705	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123499
dc.description.abstract	© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. In this paper, an ensemble method, which demonstrated efficiency in GIS based flood modeling, was used to create flood probability indices for the Damansara River catchment in Malaysia. To estimate flood probability, the frequency ratio (FR) approach was combined with support vector machine (SVM) using a radial basis function kernel. Thirteen flood conditioning parameters, namely, altitude, aspect, slope, curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, distance from river, geology, soil, surface runoff, and land use/cover (LULC), were selected. Each class of conditioning factor was weighted using the FR approach and entered as input for SVM modeling to optimize all the parameters. The flood hazard map was produced by combining the flood probability map with flood-triggering factors such as; averaged daily rainfall and flood inundation depth. Subsequently, the hydraulic 2D high-resolution sub-grid model (HRS) was applied to estimate the flood inundation depth. Furthermore, vulnerability weights were assigned to each element at risk based on their importance. Finally flood risk map was generated. The results of this research demonstrated that the proposed approach would be effective for flood risk management in the study area along the expressway and could be easily replicated in other areas.	en_US
dc.relation.ispartof	Geomatics, Natural Hazards and Risk	en_US
dc.relation.isbasedon	10.1080/19475705.2017.1294113	en_US
dc.title	Ensemble machine-learning-based geospatial approach for flood risk assessment using multi-sensor remote-sensing data and GIS	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	8	en_US
utslib.for	0406 Physical Geography and Environmental Geoscience	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	open_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	8	en_US

Abstract:

© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. In this paper, an ensemble method, which demonstrated efficiency in GIS based flood modeling, was used to create flood probability indices for the Damansara River catchment in Malaysia. To estimate flood probability, the frequency ratio (FR) approach was combined with support vector machine (SVM) using a radial basis function kernel. Thirteen flood conditioning parameters, namely, altitude, aspect, slope, curvature, stream power index, topographic wetness index, sediment transport index, topographic roughness index, distance from river, geology, soil, surface runoff, and land use/cover (LULC), were selected. Each class of conditioning factor was weighted using the FR approach and entered as input for SVM modeling to optimize all the parameters. The flood hazard map was produced by combining the flood probability map with flood-triggering factors such as; averaged daily rainfall and flood inundation depth. Subsequently, the hydraulic 2D high-resolution sub-grid model (HRS) was applied to estimate the flood inundation depth. Furthermore, vulnerability weights were assigned to each element at risk based on their importance. Finally flood risk map was generated. The results of this research demonstrated that the proposed approach would be effective for flood risk management in the study area along the expressway and could be easily replicated in other areas.

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http://hdl.handle.net/10453/123499