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孟加拉国滑坡易发性制图研究综述。

A review on landslide susceptibility mapping research in Bangladesh.

作者信息

Chowdhury Md Sharafat

机构信息

Information and Communication Technology Division, Dhaka, Bangladesh.

Department of Geography and Environment, Dhaka, Bangladesh.

出版信息

Heliyon. 2023 Jul 13;9(7):e17972. doi: 10.1016/j.heliyon.2023.e17972. eCollection 2023 Jul.

DOI:10.1016/j.heliyon.2023.e17972
PMID:37519718
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10372248/
Abstract

Landslide susceptibility mapping is a common practice for landslide susceptibility assessment across the world. Like many other mountainous areas of the world, Bangladesh is facing frequent catastrophic landslides causing severe damage to the economy and society. As a result, several types of research have been conducted on landslides in Bangladesh. In the current research, a systematic review is conducted on the existing literature related to landslide susceptibility mapping to assess its contemporary trend with global research. The publications analyzed in this research were extracted from a website comprising landslide research of Bangladesh and by manual search. The aspects of the literature considered are year of publication, the journal where published, location/size of the study area, landslide inventory data type, susceptibility assessment/mapping method, thematic variables used, DEM characteristics, accuracy assessment methods and acquired accuracy of the models. The Chi-square test was conducted and correlation was measured to assess relation between selected features and map accuracy but no significant relationship was found. The studies are concentrated into three administrative districts of Chattogram, Rangamati and Cox's Bazar mainly covering the city centre. The publication rate is increasing but not following the global trend. Though various types of models are used and compared, the application of machine and deep learning algorithms are very limited and no evidence of Physically-based methods is found. Most of the cases, landslide inventory is prepared by conducting field survey, but the size is small. The research will help future practitioner in landslide susceptibility mapping research in the area.

摘要

滑坡易发性制图是全球范围内进行滑坡易发性评估的常见做法。与世界上许多其他山区一样,孟加拉国正面临频繁的灾难性滑坡,对经济和社会造成严重破坏。因此,针对孟加拉国的滑坡开展了多种类型的研究。在当前的研究中,对与滑坡易发性制图相关的现有文献进行了系统综述,以评估其与全球研究的当代趋势。本研究中分析的出版物是从一个包含孟加拉国滑坡研究的网站以及通过手动搜索提取的。所考虑的文献方面包括出版年份、发表的期刊、研究区域的位置/规模、滑坡清单数据类型、易发性评估/制图方法、使用的主题变量、数字高程模型(DEM)特征、精度评估方法以及模型获得的精度。进行了卡方检验并测量了相关性,以评估所选特征与地图精度之间的关系,但未发现显著关系。这些研究主要集中在吉大港市、朗加马蒂和科克斯巴扎尔的三个行政区,主要覆盖市中心。出版率在上升,但未遵循全球趋势。尽管使用并比较了各种类型的模型,但机器学习和深度学习算法的应用非常有限,且未发现基于物理方法的证据。在大多数情况下,滑坡清单是通过实地调查编制的,但规模较小。该研究将有助于该地区未来从事滑坡易发性制图研究的人员。

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本文引用的文献

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Sci Rep. 2023 Jan 31;13(1):1740. doi: 10.1038/s41598-023-28991-5.
3
Influence of sampling design on landslide susceptibility modeling in lithologically heterogeneous areas.岩性非均一地区采样设计对滑坡易发性模型的影响。
Sci Rep. 2022 Feb 8;12(1):2106. doi: 10.1038/s41598-022-06257-w.
4
Deep learning-based landslide susceptibility mapping.基于深度学习的滑坡易发性制图。
Sci Rep. 2021 Dec 16;11(1):24112. doi: 10.1038/s41598-021-03585-1.
5
Optimizing the Predictive Ability of Machine Learning Methods for Landslide Susceptibility Mapping Using SMOTE for Lishui City in Zhejiang Province, China.利用 SMOTE 优化机器学习方法在浙江省丽水市滑坡易发性制图中的预测能力。
Int J Environ Res Public Health. 2019 Jan 28;16(3):368. doi: 10.3390/ijerph16030368.