Effectiveness of Investments in Prevention of Geological Disasters
Ugo Fiore (),
Zelda Marino (),
Francesca Perla (),
Mariafortuna Pietroluongo (),
Salvatore Scognamiglio () and
and Paolo Zanetti ()
Additional contact information
Ugo Fiore: Parthenope University
Zelda Marino: Parthenope University
Francesca Perla: Parthenope University
Mariafortuna Pietroluongo: Parthenope University
Salvatore Scognamiglio: Parthenope University
and Paolo Zanetti: Parthenope University
A chapter in Dynamics of Disasters, 2021, pp 101-108 from Springer
Abstract:
Abstract Research on geological disasters has made several achievements in monitoring, early warning, and risk assessment. Substantial resources are being invested in prevention projects, but, due to geographical and demographical complexity, incompleteness of data, and small number of samples, a quantitative analysis on the number of geological disasters and the entity of investments in their prevention is a difficult problem. In this work, the relation is studied between the amount of resources invested in prevention and the number of geological disasters in subsequent years. The analysis is performed on historical data, using statistical methods and a LSTM recurrent neural network.
Keywords: First keyword; Second keyword; Another keyword (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:
There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-64973-9_6
Ordering information: This item can be ordered from
http://www.springer.com/9783030649739
DOI: 10.1007/978-3-030-64973-9_6
Access Statistics for this chapter
More chapters in Springer Optimization and Its Applications from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().