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

Using open data to reveal factors of urban susceptibility to natural hazards and man-made... AbstractMulti-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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png GeoScape de Gruyter

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

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Publisher
de Gruyter
Copyright
© 2022 Alberto Vavassori et al., published by Sciendo
eISSN
1802-1115
DOI
10.2478/geosc-2022-0008
Publisher site
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Abstract

AbstractMulti-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.

Journal

GeoScape – de Gruyter

Published: Dec 1, 2022

Keywords: Disasters; Multi-hazard mapping; Urban susceptibility; Open data; Machine and deep learning

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