Using open data to reveal factors of urban susceptibility to natural hazards and man-made hazards: case of Milan and Sofia

IF 0.7 Q3 GEOGRAPHY
GeoScape Pub Date : 2022-12-01 DOI:10.2478/geosc-2022-0008
A. Vavassori, Angelly De Jesús Pugliese Viloria, M. Brovelli
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引用次数: 0

Abstract

Abstract Multi-hazard mapping in urban areas is relevant for preventing and mitigating the impact of nature- and human-induced disasters while being a challenging task as different competencies have to be put together. Artificial intelligence models are being increasingly exploited for single-hazard susceptibility mapping, from which multi-hazard maps are ultimately derived. Despite the remarkable performance of these models, their application requires the identification of a list of conditioning factors as well as the collection of relevant data and historical inventories, which may be non-trivial tasks. The objective of this study is twofold. First, based on a review of recent publications, it identifies conditioning factors to be used as an input to machine and deep learning techniques for singlehazard susceptibility mapping. Second, it investigates open datasets describing those factors for two European cities, namely Milan (Italy) and Sofia (Bulgaria) by exploiting local authorities’ databases. Identification of the conditioning factors was carried out through the review of recent publications aiming at hazard mapping with artificial intelligence models. Two indicators were conceived to define the relevance of each factor. A first research result consists of a relevance-sorted list of conditioning factors per hazard as well as a set of open and free access data describing several factors for Milan and Sofia. Based on data availability, a feasibility analysis was carried out to investigate the possibility to model hazard susceptibility for the two case studies as well as for the limit case of a city with no local data available. Results show major differences between Milan and Sofia while pointing out Copernicus services’ datasets as a valuable resource for susceptibility mapping in case of limited local data availability. Achieved outcomes have to be intended as preliminary results, as further details shall be disclosed after the discussion with domain experts.
利用开放数据揭示城市对自然灾害和人为灾害的易感性因素:以米兰和索非亚为例
摘要城市地区的多灾害测绘与预防和减轻自然灾害和人为灾害的影响有关,同时也是一项具有挑战性的任务,因为必须综合不同的能力。人工智能模型正越来越多地被用于单危害易感性地图绘制,最终从中得出多危害地图。尽管这些模型具有显著的性能,但其应用需要确定一系列条件因素,并收集相关数据和历史清单,这可能是一项不平凡的任务。这项研究的目的是双重的。首先,基于对最近出版物的回顾,它确定了条件因素,将其用作机器和深度学习技术的输入,用于单危险易感性映射。其次,它通过利用地方当局的数据库,调查了描述两个欧洲城市,即米兰(意大利)和索菲亚(保加利亚)的这些因素的开放数据集。通过审查旨在利用人工智能模型绘制危害图的最新出版物,确定了条件因素。设想了两个指标来界定每个因素的相关性。第一个研究结果包括每个危险的条件因素的相关性排序列表,以及一组描述米兰和索菲亚几个因素的开放和免费数据。根据数据可用性,进行了可行性分析,以调查为两个案例研究以及没有当地数据可用的城市的极限情况建模危险易感性的可能性。结果显示了米兰和索菲亚之间的主要差异,同时指出,在当地数据可用性有限的情况下,哥白尼服务的数据集是易感性映射的宝贵资源。已取得的成果必须作为初步成果,在与领域专家讨论后应披露更多细节。
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
GeoScape
GeoScape GEOGRAPHY-
CiteScore
2.70
自引率
7.70%
发文量
7
审稿时长
4 weeks
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