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Earth Systems and Environment

, Volume 3, Issue 3, pp 585–601 | Cite as

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

  • Mahfuzur Rahman
  • Chen NingshengEmail author
  • Md Monirul Islam
  • Ashraf Dewan
  • Javed Iqbal
  • Rana Muhammad Ali Washakh
  • Tian Shufeng
Original Article
  • 47 Downloads

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.

Keywords

AHP ANN Bangladesh Flood susceptibility map FR LR 

Notes

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].

Compliance with Ethical Standards

Conflict of Interest

On behalf of all the authors, the corresponding author states that there is no conflict of interest.

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Copyright information

© King Abdulaziz University and Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mahfuzur Rahman
    • 1
    • 2
    • 3
  • Chen Ningsheng
    • 1
    Email author
  • Md Monirul Islam
    • 3
  • Ashraf Dewan
    • 4
  • Javed Iqbal
    • 1
    • 5
  • Rana Muhammad Ali Washakh
    • 1
    • 2
  • Tian Shufeng
    • 1
    • 2
  1. 1.Key Laboratory for Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and EnvironmentChinese Academy of Sciences (CAS)ChengduPeople’s Republic of China
  2. 2.University of Chinese Academy of SciencesBeijingPeople’s Republic of China
  3. 3.Department of Civil EngineeringInternational University of Business Agriculture and Technology (IUBAT)DhakaBangladesh
  4. 4.Spatial Sciences Discipline, School of Earth and Planetary SciencesCurtin UniversityBentleyAustralia
  5. 5.Department of Earth SciencesAbbottabad University of Science and TechnologyAbbottabadPakistan

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