Flood Susceptibility Mapping Using GIS-Based Frequency Ratio and Shannon’s Entropy Index Bivariate Statistical Models: A Case Study of Chandrapur District, India

IF 2.8 3区 地球科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Asheesh Sharma, Mandeep Poonia, Ankush Rai, Rajesh B. Biniwale, Franziska Tügel, Ekkehard Holzbecher, Reinhard Hinkelmann
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Abstract

Flooding poses a significant threat as a prevalent natural disaster. To mitigate its impact, identifying flood-prone areas through susceptibility mapping is essential for effective flood risk management. This study conducted flood susceptibility mapping (FSM) in Chandrapur district, Maharashtra, India, using geographic information system (GIS)-based frequency ratio (FR) and Shannon’s entropy index (SEI) models. Seven flood-contributing factors were considered, and historical flood data were utilized for model training and testing. Model performance was evaluated using the area under the curve (AUC) metric. The AUC values of 0.982 for the SEI model and 0.966 for the FR model in the test dataset underscore the robust performance of both models. The results revealed that 5.4% and 8.1% (FR model) and 3.8% and 7.6% (SEI model) of the study area face very high and high risks of flooding, respectively. Comparative analysis indicated the superiority of the SEI model. The key limitations of the models are discussed. This study attempted to simplify the process for the easy and straightforward implementation of FR and SEI statistical flood susceptibility models along with key insights into the flood vulnerability of the study region.
使用基于地理信息系统的频率比和香农熵指数双变量统计模型绘制洪水易感性地图:印度钱德拉布尔地区案例研究
洪水作为一种普遍存在的自然灾害,对人类构成了严重威胁。为减轻其影响,通过绘制易受洪水影响地区图来确定易受洪水影响地区对于有效管理洪水风险至关重要。本研究采用基于地理信息系统(GIS)的频率比(FR)和香农熵指数(SEI)模型,在印度马哈拉施特拉邦钱德拉布尔地区绘制了洪水易发区地图(FSM)。研究考虑了七个洪水诱因,并利用历史洪水数据对模型进行了训练和测试。使用曲线下面积(AUC)指标对模型性能进行了评估。在测试数据集中,SEI 模型的 AUC 值为 0.982,FR 模型的 AUC 值为 0.966,这表明这两个模型的性能都很稳定。结果显示,研究区域分别有 5.4%和 8.1%(FR 模型)以及 3.8%和 7.6%(SEI 模型)的地区面临极高和高洪水风险。对比分析表明,SEI 模型更具优势。本文讨论了模型的主要局限性。本研究试图简化 FR 和 SEI 统计洪水易感性模型的实施过程,使其简单易行,并对研究区域的洪水易感性提供重要启示。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ISPRS International Journal of Geo-Information
ISPRS International Journal of Geo-Information GEOGRAPHY, PHYSICALREMOTE SENSING&nb-REMOTE SENSING
CiteScore
6.90
自引率
11.80%
发文量
520
审稿时长
19.87 days
期刊介绍: ISPRS International Journal of Geo-Information (ISSN 2220-9964) provides an advanced forum for the science and technology of geographic information. ISPRS International Journal of Geo-Information publishes regular research papers, reviews and communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced. The 2018 IJGI Outstanding Reviewer Award has been launched! This award acknowledge those who have generously dedicated their time to review manuscripts submitted to IJGI. See full details at http://www.mdpi.com/journal/ijgi/awards.
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