Back to Search Start Over

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

Authors :
Mojaddadi, Hossein
Pradhan, Biswajeet
Nampak, Haleh
Ahmad, Noordin
Ghazali, Abdul Halim bin
Source :
Geomatics, Natural Hazards & Risk; Dec2017, Vol. 8 Issue 2, p1080-1102, 23p, 1 Color Photograph, 1 Diagram, 4 Charts, 2 Graphs, 9 Maps
Publication Year :
2017

Abstract

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. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19475705
Volume :
8
Issue :
2
Database :
Complementary Index
Journal :
Geomatics, Natural Hazards & Risk
Publication Type :
Academic Journal
Accession number :
126496824
Full Text :
https://doi.org/10.1080/19475705.2017.1294113