Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh

Mahfuzur Rahman; Ningsheng Chen; Ahmed Elbeltagi; Md Monirul Islam; Mehtab Alam; Hamid Reza Pourghasemi; Wang Tao; Jun Zhang; Tian Shufeng; Hamid Faiz; Muhammad Aslam Baig; Ashraf Dewan

doi:10.1016/j.jenvman.2021.113086

Application of stacking hybrid machine learning algorithms in delineating multi-type flooding in Bangladesh

J Environ Manage. 2021 Oct 1:295:113086. doi: 10.1016/j.jenvman.2021.113086. Epub 2021 Jun 18.

Authors

Affiliations

¹ Key Laboratory for Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS), Chengdu, 610041, China; University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China; Department of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh. Electronic address: mfz.rahman@iubat.edu.
² Key Laboratory for Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS), Chengdu, 610041, China; University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China; Academy of Plateau Science and Sustainability, Xining, 810016, China. Electronic address: chennsh@imde.ac.cn.
³ Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt; College of Environmental and Resource Sciences, Zhejiang University, Hangzhou, 310058, China.
⁴ Department of Civil Engineering, International University of Business Agriculture and Technology, Dhaka, 1230, Bangladesh. Electronic address: mmislam@iubat.edu.
⁵ Key Laboratory for Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS), Chengdu, 610041, China; University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China.
⁶ Department of Natural Resources and Environmental Engineering, College of Agriculture, Shiraz University, Shiraz, Iran. Electronic address: hr.pourghasemi@shirazu.ac.ir.
⁷ Key Laboratory for Mountain Hazards and Earth Surface Process, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences (CAS), Chengdu, 610041, China.
⁸ School of Earth and Planetary Sciences, Curtin University, Kent St, Bentley, WA, 6102, Australia. Electronic address: A.Dewan@curtin.edu.au.

PMID: 34153582
DOI: 10.1016/j.jenvman.2021.113086

Abstract

Floods are among the most devastating natural hazards in Bangladesh. The country experiences multi-type floods (i.e., fluvial, flash, pluvial, and surge floods) every year. However, areas prone to multi-type floods have not yet been assessed on a national scale. Here, we used locally weighted linear regression (LWLR), random subspace (RSS), reduced error pruning tree (REPTree), random forest (RF), and M5P model tree algorithms in a hybrid ensemble to assess multi-type flood probabilities at a national scale in Bangladesh. We used historical flood data (1988-2020), remote sensing images (e.g., MODIS, Landsat 5-8, and Sentinel-1), and topography, hydrogeology, and environmental datasets to train and validate the proposed algorithms. According to the results, the stacking ensemble machine learning LWLR-RF algorithm performed better than the other algorithms in predicting flood probabilities, with R² = 0.967-0.999, MAE = 0.022-0.117, RMSE = 0.029-0.148, RAE = 4.48-23.38%, and RRSE = 5.8829.69% for the training and testing datasets. Furthermore, true skill statistics (TSS: 0.929-0.967), corrected classified instances (CCI: 96.45-98.35), area under the curve (AUC: 0.983-0.997), and Gini coefficients (0.966-0.994) were computed to validate the constructed (LWLR-RF) multi-type flood probability maps. The maps constructed via the LWLR-RF algorithm revealed that the proportions of different categories of flooding areas in Bangladesh are fluvial flooding 1.50%, 5.71%, 12.66%, and 13.77% of the total land area; flash floods of 4.16%, 8.90%, 11.11%, and 5.07%; pluvial flooding: 5.72%, 3.25%, 5.07%, and 0.90%; and surge flooding, 1.69%, 1.04%, 0.52%, and 8.64% of the total land area, respectively. These percentages represent low, medium, high, and very high probabilities of flooding. The findings can guide future flood risk management and sustainable land-use planning in the study area.

Keywords: Extreme floods; Machine learning; National scale; Risk management; Stacking algorithm.

MeSH terms

Algorithms
Bangladesh
Floods*
Machine Learning*
Probability