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

Publication Type:
Journal Article
Citation:
Geomatics, Natural Hazards and Risk, 2017, 8 (2), pp. 1080 - 1102
Issue Date:
2017-12-15
Metrics:
Full metadata record
Files in This Item:
Filename Description Size
4_16_2018_Ensemble m.pdfPublished Version3.43 MB
Adobe PDF
© 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.
Please use this identifier to cite or link to this item: