Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Maria Antonia Pedone"'
Autor:
Filippo Balestra, Michele Del Vecchio, Dina Pirone, Maria Antonia Pedone, Danilo Spina, Salvatore Manfreda, Giovanni Menduni, Daniele Fabrizio Bignami
Publikováno v:
Environmental Sciences Proceedings, Vol 21, Iss 1, p 36 (2022)
This study suggests a rapid methodology to delineate areas prone to flood using machine learning techniques. Based on available historically flooded areas, the model employs and combines globally collectible and reproducible conditioning factors to a
Externí odkaz:
https://doaj.org/article/e6f7082d5d5540e5bf26048a4ebf2713
Autor:
Michele Del Vecchio, Filippo Balestra, Maria Antonia Pedone, Dina Pirone, Danilo Spina, Salvatore Manfreda, Giovanni Menduni
In Italy, flood hazard maps are traditionally obtained through hydrologic-hydraulic modelling. In fact, numerical simulations are usually limited to the main river or specific tributaries leaving a significant part of the territory unclassified. Ther
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::68433bf15c61db51f6e9cc84250c9718
https://doi.org/10.5194/egusphere-egu22-12328
https://doi.org/10.5194/egusphere-egu22-12328
The concept of “flood susceptibility” is generally used to identify the flood prone areas. The flood susceptibility defines the probability of a territory to be flooded, and generally is determined according to its geo-litho-morphological and cli
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::551248c42ad550051b51c8a10831e919
https://doi.org/10.5194/egusphere-egu2020-22470
https://doi.org/10.5194/egusphere-egu2020-22470