On the relationship between energy-related plants and oncological cases in Basilicata (Italy) using soft computing methods

Salvatore Rampone () and Biagio Simonetti
Additional contact information
Salvatore Rampone: Università del Sannio
Biagio Simonetti: Università del Sannio

Quality & Quantity: International Journal of Methodology, 2020, vol. 54, issue 5, No 2, 1387-1399

Abstract: Abstract In Basilicata (Southern Italy), in areas around energy-related plants, including oil extraction sites, oil refineries, and underground gas storage plants, we consider a set of annual air quality measurements, the analysis of toxic substances emitted, and the percentage of tumours with respect the habitants. Artificial Neural Networks and Genetic Programming are then applied in order to assess the data correlation and to estimate the tumour percentage in the next years. The approach is tested using a tenfold cross validation methodology. Both the used soft computing methods show low error rates and high correlation measures. Furthermore the Genetic Programming evidences an explicit representation of the factors that favour the tumours. The results push the attention towards the prevention of potential health impacts among Basilicata residents living close to the plants.

Keywords: Basilicata (Italy); Oil extraction and refinement; Gas processing and storage; Environment pollution; Tumours; Soft computing; Artificial neural networks; Genetic programming (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s11135-019-00866-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:qualqt:v:54:y:2020:i:5:d:10.1007_s11135-019-00866-w

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11135

DOI: 10.1007/s11135-019-00866-w

Access Statistics for this article

Quality & Quantity: International Journal of Methodology is currently edited by Vittorio Capecchi

More articles in Quality & Quantity: International Journal of Methodology from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().