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Flood Susceptibility Assessment in Bangladesh Using Machine Learning and Multi-criteria Decision Analysis

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Abstract

This work proposes a new approach by integrating statistical, machine learning, and multi-criteria decision analysis, including artificial neural network (ANN), logistic regression (LR), frequency ratio (FR), and analytical hierarchy process (AHP). Dependent (flood inventory) and independent variables (flood causative factors) were prepared using remote sensing data and the Mike-11 hydrological model and secondary data from different sources. The flood inventory map was randomly divided into training and testing datasets, where 334 flood locations (70%) were used for training and the remaining 141 locations (30%) were employed for testing. Using the area under the receiver operating curve (AUROC), predictive power of the model was tested. The results revealed that LR model had the highest success rate (81.60%) and prediction rate (86.80%), among others. Furthermore, different combinations of the models were evaluated for flood susceptibility mapping and the best combination (11C) was used for generating a new flood hazard map for Bangladesh. The performance of the 11C integrated models was also evaluated using the AUROC and found that integrated LR-FR model had the highest predictive power with an AUROC value of 88.10%. This study offers a new opportunity to the relevant authority for planning and designing flood control measures.

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Acknowledgements

The authors acknowledge and appreciate the provision of rainfall data by the Bangladesh Water Development Board (BWDB), without which this study would not have been possible. Thanks to AFM Kamal Chowdhury, Nirdesh Nepal and Soumik Nafis Sadeek for their valuable comments which helped us to improve the quality of the manuscript. This research was funded by the National Natural Science Foundation of China [Grant no. 41861134008 and 41671112] and the 135 Strategic Program of the Institute of Mountain Hazards and Environment (IMHE), Chinese Academy of Sciences (CAS) [Grant no. SDS-135-1705].

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Authors and Affiliations

  1. Key Laboratory for Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS), Chengdu, 610041, People’s Republic of China

    Mahfuzur Rahman, Chen Ningsheng, Javed Iqbal, Rana Muhammad Ali Washakh & Tian Shufeng

  2. University of Chinese Academy of Sciences, Beijing, 100049, People’s Republic of China

    Mahfuzur Rahman, Rana Muhammad Ali Washakh & Tian Shufeng

  3. Department of Civil Engineering, International University of Business Agriculture and Technology (IUBAT), Dhaka, Bangladesh

    Mahfuzur Rahman & Md Monirul Islam

  4. Spatial Sciences Discipline, School of Earth and Planetary Sciences, Curtin University, Kent St, Bentley, WA, 6102, Australia

    Ashraf Dewan

  5. Department of Earth Sciences, Abbottabad University of Science and Technology, Abbottabad, Pakistan

    Javed Iqbal

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  1. Mahfuzur Rahman
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  2. Chen Ningsheng
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  3. Md Monirul Islam
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Correspondence to Chen Ningsheng.

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Rahman, M., Ningsheng, C., Islam, M.M. et al. Flood Susceptibility Assessment in Bangladesh Using Machine Learning and Multi-criteria Decision Analysis. Earth Syst Environ 3, 585–601 (2019). https://doi.org/10.1007/s41748-019-00123-y

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