Utilizing User-Generated Content and GIS for Flood Susceptibility Modeling in Mountainous Areas: A Case Study of Jian City in China

Zhongping Zeng, Yujia Li, Jinyu Lan and Abdur Rahim Hamidi
Additional contact information
Zhongping Zeng: College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China
Yujia Li: College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China
Jinyu Lan: College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China
Abdur Rahim Hamidi: College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China

Sustainability, 2021, vol. 13, issue 12, 1-18

Abstract: Floods are threats seriously affecting people’s lives and property globally. Risk analysis such as flood susceptibility assessment is one of the critical approaches to mitigate flood impacts. However, the inadequate field survey and lack of data might hinder the mapping of flood susceptibility. The emergence of user-generated content (UGC) in the era of big data provides new opportunities for flood risk management. This research proposed a flood susceptibility assessment model using UGC as a potential data source and conducted empirical research in Ji’an County in China to make up for the lack of ground survey data in mountainous-hilly areas. This article used python crawlers to obtain the geographic location of the floods in Ji’an City from 2016 to 2019 from social media, and the state-of-the-art MaxEnt algorithm was adopted to obtain the flood occurrence map. The map was verified by the flood data crawled from reliable official media, which achieved an average AUC of 0.857% and an overall accuracy of 93.1%. Several novel indicators were used to evaluate the importance of conditioning factors from different perspectives. Land use, slope, and distance from the river were found to contribute most to the occurrence of floods. Our findings have shown that the proposed historical UG C-based model is practical and has good flood-risk-mapping performance. The importance of the conditioning factors to the occurrence of floods can also be ranked. The reports from stakeholders are a great supplement to the insufficient field survey data and tend to be valuable resources for flood disaster preparation and mitigation in the future. Finally, the limitations and future development directions of UGC as a data source for flood risk assessment are discussed.

Keywords: floods; user-generated content; susceptibility assessment; MaxEnt; mountainous areas (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/13/12/6929/pdf (application/pdf)
https://www.mdpi.com/2071-1050/13/12/6929/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:13:y:2021:i:12:p:6929-:d:578219

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().