Department of Chemical and Environmental Technology, ESCET, Rey Juan Carlos University, C/Tulipán s/n, 28933 Móstoles, Madrid, Spain.
Departamento de Geografía, Geología y Medio Ambiente, Facultad de Filosofía y Letras, Universidad de Alcalá, Área de Geografía, GITA, C/Colegios 2, 28801 Alcalá de Henares, Madrid, Spain.
Int J Environ Res Public Health. 2021 Nov 15;18(22):11987. doi: 10.3390/ijerph182211987.
Among the numerous natural hazards, landslides are one of the greatest, as they can cause enormous loss of life and property, and affect the natural ecosystem and their services. Landslides are disasters that cause damage to anthropic activities and innumerable loss of human life, globally. The landslide risk assessed by the integration of susceptibility and vulnerability maps has recently become a manner of studying sites prone to landslide events and managing these regions well. Developing countries, where the impact of landslides is frequent, need risk assessment tools that enable them to address these disasters, starting with their prevention, with free spatial data and appropriate models. Our study shows a heuristic risk model by integrating a susceptibility map made by AutoML and a vulnerability one that is made considering ecological vulnerability and socio-economic vulnerability. The input data used in the State of Guerrero (México) approach uses spatial data, such as remote sensing, or official Mexican databases. This aspect makes this work adaptable to other parts of the world because the cost is low, and the frequency adaptation is high. Our results show a great difference between the distribution of vulnerability and susceptibility zones in the study area, and even between the socio-economic and ecological vulnerabilities. For instance, the highest ecological vulnerability is in the mountainous zone in Guerrero, and the highest socio-economic vulnerability values are found around settlements and roads. Therefore, the final risk assessment map is an integrated index that considers susceptibility and vulnerability and would be a good first attempt to challenge landslide disasters.
在众多自然灾害中,滑坡是最严重的灾害之一,因为它们可能造成巨大的生命和财产损失,并影响自然生态系统及其服务。滑坡是对人类活动造成破坏并导致无数人员伤亡的灾害,在全球范围内都是如此。通过将易发性和脆弱性图进行整合来评估滑坡风险,已成为研究易发生滑坡事件的地点并对这些地区进行良好管理的一种方式。在经常发生滑坡影响的发展中国家,需要有风险评估工具来应对这些灾害,从预防开始,利用免费的空间数据和适当的模型。我们的研究通过整合由 AutoML 生成的易发性图和考虑生态脆弱性和社会经济脆弱性的脆弱性图,展示了一个启发式风险模型。在格雷罗州(墨西哥)的方法中使用的输入数据使用了空间数据,如遥感或墨西哥官方数据库。这方面使得这项工作可以适用于世界其他地区,因为成本低,而且频率适应性高。我们的研究结果表明,研究区域内脆弱性和易发性区域的分布存在很大差异,甚至社会经济和生态脆弱性之间也存在差异。例如,格雷罗州山区的生态脆弱性最高,而居民点和道路周围的社会经济脆弱性值最高。因此,最终的风险评估图是一个综合指数,考虑了易发性和脆弱性,这将是一个很好的尝试来应对滑坡灾害。