Assessment of flood susceptibility mapping using support vector machine, logistic regression and their ensemble techniques in the Belt and Road region

Abstract

Floods have occurred frequently all over the world. During 2000–2020, nearly half (44.9%) of global floods occurred in the Belt and Road region because of its complex geology, topography, and climate. Therefore, providing an insight into the spatial distribution characteristics of flood susceptibility in this region is essential. Here, a database was established with 11 flood conditioning factors, 1500 flooded points, and 1500 non-flooded points selected by an improved method. Subsequently, a rare combination of logistic regression and support vector machine, integrated by heterogeneous framework, was applied to generate an ensemble flood susceptibility map. Based on it, the concept of ecological vulnerability synthesis index in the ecological field was introduced into this study, and the flood susceptibility comprehensive index (FSCI) was proposed to quantify the degree of flood susceptibility of each country and sub-region. At the results, the ensemble model has an excellent accuracy, with the highest AUC value of 0.9342. The highest and high flood susceptibility zones are mainly located in the southeastern part of Eastern Asia, most of Southeast Asia and South Asia, account for 12.22% and 9.57% of the total study area, respectively. From the regional perspective, it can be found that Southeast Asia had the highest flood susceptibility with the highest FSCI of 4.69, while East Asia and Central and Eastern Europe showed the most significant spatial distribution characteristics. From the national perspective, of the 66 countries in this region, 20 of the countries have the highest flood susceptibility level (FSCI_n > 0.8), which face the greatest threat of flooding. These results are able to facilitate reasonable flood mitigation measures develop at the most critical locations in the Belt and Road region and lays a theoretical basis for quantifying flood susceptibility at national or regional scale.

Keywords:

• Food susceptibility
• ensemble modeling
• support vector machine
• logistic regression
• the Belt and Road region

Disclosure statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Authors’ contributions

JL, YL, and YFH were responsible for the collection and processing of the dataset. JL and JNX conceptualized the study and developed the methodology. JL and JNX were responsible for the analysis and validation of the results, and finished the original draft preparation. JL, JNX, WMC, YFC, YD, 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.

Data availability statement

Flood inventory map are available at http://floodobservatory.colorado.edu/ (April 2020). DEM data are available at https://srtm.csi.cgiar.org/srtmdata/ (last access: September 2017). Precipitation data (GPM) are available at https://disc.gsfc.nasa.gov/datasets/GPM_3IMERGDE_06/summary?keywords=GPM/ (last access: October 2019). River Density data are available at https://www.openstreetmap.org/ (last access: December 2019). Land cover data (MCD12Q1) are available at https://ladsweb.modaps.eosdis.nasa.gov/search/ (last access: December 2019). Fractional vegetation cover data (MOD13Q1) are available at https://ladsweb.modaps.eosdis.nasa.gov/search/ (last access: December 2019). Impervious surface data are available at https://ghslsys.jrc.ec.europa.eu/ (last access: December 2019). Soil texture data are available at http://www.fao.org/ (last access: December 2019).

Additional information

Funding

This paper is supported by Key R & D project of Sichuan Science and Technology Department (Grant No. 2021YFQ0042), Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA20030302), 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.