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

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) us...

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Veröffentlicht in:	Geomatics, natural hazards and risk natural hazards and risk, 2017-12, Vol.8 (2), p.1080-1102
Hauptverfasser:	Mojaddadi, Hossein, Pradhan, Biswajeet, Nampak, Haleh, Ahmad, Noordin, Ghazali, Abdul Halim bin
Format:	Artikel
Sprache:	eng
Schlagworte:	2D HRS Basis functions Catchment area Conditioning Curvature Daily precipitation Depth perception Environmental risk Flood control Flood hazards Flood management Flood mapping Flood probability Flood risk Flood risk assessment Floods Geographical information systems Geology GIS hazard inundation Land use Learning algorithms Machine learning Malaysia Mathematical models Modelling Moisture content Parameters Probability theory Radial basis function Rain Rainfall Remote sensing Remote sensors Risk assessment Risk management River catchments Rivers Roughness Runoff Sediment transport Soil Support vector machines Surface runoff SVM Topography Two dimensional models Vulnerability Wetness index
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