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

By H. Mojaddadi, Biswajeet Pradhan, Haleh Nampak, N. Ahmad, Abdul Halim Ghazali. 2017

408 citations recorded by the source corpus.

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.

Subfields: Global and Planetary Change, Water Science and Technology

View Paper: https://www.semanticscholar.org/paper/3a26b31e6c3699b7b003530f6666d3c96322b096

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Citations in the corpus

Evaluating the application of the statistical index method in flood susceptibility mapping and its comparison with frequency ratio and logistic regression methods (2018, methodology): “…presented in the literature, artificial neural networks (ANNs), frequency ratio (FR), logistic regression (LR), decision trees (DT), and support vector machines (SVMs) are the most popular techniques that have been utilized in flood domain mapping (Tehrany et al. 2015b; Mojaddadi et al. 2017).”
Applications of machine learning methods for engineering risk assessment – A review (2020, background): “For example, Mojaddadi et al. (2017) propose an ensemble machine-learning approach to determine the level of risk of flood for a given geographical area.”