Using Satellite Imagery to Improve Local Pollution Models for High-Voltage Transmission Lines and Insulators

Peter Krammer, Marcel Kvassay, Ján Mojžiš, Martin Kenyeres, Miloš Očkay, Ladislav Hluchý, Ľuboš Pavlov and Ľuboš Skurčák
Additional contact information
Peter Krammer: Institute of Informatics, Slovak Academy of Sciences, Dúbravská Cesta 9, 845 07 Bratislava, Slovakia
Marcel Kvassay: Institute of Informatics, Slovak Academy of Sciences, Dúbravská Cesta 9, 845 07 Bratislava, Slovakia
Ján Mojžiš: Institute of Informatics, Slovak Academy of Sciences, Dúbravská Cesta 9, 845 07 Bratislava, Slovakia
Martin Kenyeres: Institute of Informatics, Slovak Academy of Sciences, Dúbravská Cesta 9, 845 07 Bratislava, Slovakia
Miloš Očkay: Institute of Informatics, Slovak Academy of Sciences, Dúbravská Cesta 9, 845 07 Bratislava, Slovakia
Ladislav Hluchý: Institute of Informatics, Slovak Academy of Sciences, Dúbravská Cesta 9, 845 07 Bratislava, Slovakia
Ľuboš Pavlov: VUJE, a.s., 918 64 Trnava, Slovakia
Ľuboš Skurčák: VUJE, a.s., 918 64 Trnava, Slovakia

Future Internet, 2022, vol. 14, issue 4, 1-17

Abstract: This paper addresses the regression modeling of local environmental pollution levels for electric power industry needs, which is fundamental for the proper design and maintenance of high-voltage transmission lines and insulators in order to prevent various hazards, such as accidental flashovers due to pollution and the resultant power outages. The primary goal of our study was to increase the precision of regression models for this application area by exploiting additional input attributes extracted from satellite imagery and adjusting the modeling methodology. Given that thousands of different attributes can be extracted from satellite images, of which only a few are likely to contain useful information, we also explored suitable feature selection procedures. We show that a suitable combination of attribute selection methods (relief, FSRF-Test, and forward selection), regression models (random forest models and M5P regression trees), and modeling methodology (estimating field-measured values of target variables rather than their upper bounds) can significantly increase the total modeling accuracy, measured by the correlation between the estimated and the true values of target variables. Specifically, the accuracies of our regression models dramatically rose from 0.12–0.23 to 0.40–0.64, while their relative absolute errors were conversely reduced (e.g., from 1.04 to 0.764 for the best model).

Keywords: regression modeling; electric power industry; satellite images; attribute selection; local pollution modeling; stochastic variable; model refinement (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/14/4/99/pdf (application/pdf)
https://www.mdpi.com/1999-5903/14/4/99/ (text/html)

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:gam:jftint:v:14:y:2022:i:4:p:99-:d:777314

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().