Abstract
Flood is one of the most serious natural disasters in the world. Flood losses in the developing countries throughout the Belt and Road region are more than twice the global average. However, to date, the extent of the vulnerability of the Belt and Road region remains poorly understood. Therefore, this study sought to address this knowledge gap. In this study, we presented a vulnerability assessment model based on triangular fuzzy number-based analytic hierarchy process (TFN-AHP) and support vector machine (SVM) model. Firstly, a geospatial database including 11 flood conditioning factors was built. Secondly, the exposure and disaster reduction capability were calculated based on TFN-AHP and SVM, respectively. Finally, the spatial distribution of vulnerability throughout the Belt and Road region was generated. According to the results, the exposure and disaster reduction capability in most areas are extremely low, accounting for 86.45% and 80.53%, respectively. Meanwhile, the vulnerability of 47,105,300 km2 areas is low or extremely low, accounting for 93% of the Belt and Road region. The high-vulnerable areas (accounting for 3.54%) are primarily concentrated in the southern and eastern parts of China, northern India, most areas of Bangladesh, the Indus Valley in Pakistan, the Nile River Basin in Egypt, and the central region of Indonesia. Obviously, these regions with high vulnerability are characterized by frequent economic activities and dense populations. As suggested of these results, this study provides scientific and technological evidence for the prevention and mitigation of flood disasters in the countries along the Belt and Road region.
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Data availability
The population data are available at https://www.worldpop.org/ (last access: April 2019). The land use data are available at https://ladsweb.modaps.eosdis.nasa.gov/ (last access: September 2019). The GDP data are available at https://datadryad.org/stash/dataset/doi:10.5061/dryad.dk1j0 (last access: October 2019). The data connected with infrastructure, including data from hospitals, shelters, and road density data, are available at https://www.openstreetmap.org/ (last access: December 2019). The digital elevation model (DEM) data are available at http://srtm.csi.cgiar.org/srtmdata/ (last access: September 2018). The impervious surface data are available at https://ghslsys.jrc.ec.europa.eu/ (last access: December 2019).
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Funding
This study is supported by Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA20030302), Key R & D project of Sichuan Science and Technology Department (Grant No. 2021YFQ0042), National Flash Flood Investigation and Evaluation Project (Grant No. SHZH-IWHR-57), National Key R&D Program of China (2020YFD1100701), and the Science and Technology Project of Xizang Autonomous Region (Grant No. XZ201901-GA-07), Project form Science and Technology Bureau of Altay Region in Yili Kazak Autonomous Prefecture. The authors are grateful to this support.
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YD, YL, and YFH were responsible for the collection and processing of the dataset. YD and JNX conceptualized the study and developed the methodology. YD and JNX were responsible for the analysis and validation of the results and finished the original draft preparation. YD, JNX, WMC, NW, JL, WH, GY all participated in the reviewing of methodology, results, and article. All authors contributed to paper preparation and agreed to the published version of the manuscript.
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Duan, Y., Xiong, J., Cheng, W. et al. Flood vulnerability assessment using the triangular fuzzy number-based analytic hierarchy process and support vector machine model for the Belt and Road region. Nat Hazards 110, 269–294 (2022). https://doi.org/10.1007/s11069-021-04946-9
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DOI: https://doi.org/10.1007/s11069-021-04946-9