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

Hossein Mojaddadi,Biswajeet Pradhan,Haleh Nampak,Noordin Ahmad,Abdul Halim Bin Ghazali

doi:10.1080/19475705.2017.1294113

Hossein Mojaddadi, Biswajeet Pradhan + Show 3 more

Open Access

https://doi.org/10.1080/19475705.2017.1294113

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Abstract

ABSTRACTIn 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.

Highlights

Flood events are typically regarded to be the most common natural disaster worldwide (Stefanidis & Stathis 2013)
For the topographic wetness index (TWI) factor, the class range of 0.96–5.47 exhibits the lowest correlation (0.49) with flooding while class 9.89–20.44 has the highest coronation with flood which means more likely to be wet
In the case of the topographic roughness index (TRI) and sediment transport index (STI) factors, the class ranges of 0–0.05 and 0–0.72 obtain the highest frequency ratio (FR) values at 1.46 and 1.44, respectively, which show in smooth area such as plain and alluvial type of river, the occurrence of flood is high and support the accuracy of this model

Summary

Introduction

Flood events are typically regarded to be the most common natural disaster worldwide (Stefanidis & Stathis 2013). Flood risk management is an important challenge in many cities. Population growth, economic development, and climate change will increase the magnitude of this challenge (Huong & Pathirana 2013). Considerable and irreparable damages to farmlands, transportation, bridges, and many other aspects of urban infrastructure prove the urgent requirement for flood control and prevention (Tehrany et al 2014; Pradhan et al 2014). Flood risk analysis and risk mitigation are two components of flood risk management. Flood risk analysis aims to investigate where the risk of flood occurrence is unacceptably high and where risk mitigation actions are required. Comprehensive flood risk analysis by detecting hazardous and risky areas is an essential part of risk management to estimate the amount of damages that can occur because of flooding (Meyer et al, 2008)

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