Industry 4.0
Vol. 8 (2023), Issue 3, pg(s) 75-78

TECHNOLOGICAL BASIS OF “INDUSTRY 4.0”

Guidelines for the application of artificial intelligence in the study of the influence of climate change on transport infrastructure

  • 1 UACEG – Sofia, Bulgaria

Abstract

We are witnessing the massive and impressive penetration of artificial intelligence (AI) into many areas of human activity. This process is expected to intensify in the next few decades. In most technical fields, there will be a preponderance of the so-called narrow artificial intelligence with clearly defined tasks and functions. It is usually a coherent set of neural networks trained to solve specific problems. The advantage of narrow AI is that it is entirely controllable and, at the same time, has excellent capabilities. This publication aims to outline guidelines for applying narrow artificial intelligence in investigating the impact of climate change on transport infrastructure. After a brief introduction to narrow artificial intelligence and climate change, various possible areas suitable for AI modeling are explored. Directions and preparatory tasks for collecting climate-sensitive local data on the condition and changes in the transport infrastructure in Bulgaria necessary for AI training are identified.

Keywords

References

  1. Kaplan, A., Haenlein, M. Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 2018 (62) 10.1016/j.bushor.2018.08.004.
  2. Ivanova, E., Matev, S., Ivanova, S. Impact of climate changes on transport infrastructure in Bulgaria, Annual of the University of Architecture, Civil engineering and Geodesy – Sofia, Vol. 56 (2023), Issue 1, pp. 117-131.
  3. Large Language model chatGPT https://chat.openai.com/
  4. Large Language model Bing https://www.bing.com/
  5. Fjelland R., Why general artificial intelligence will not be realized, Humanities and Social Sciences Communications, Vol. 7, (2020), Article number: 10.
  6. Kumpulainen, S., Terziyan, V. Artificial General Intelligence vs. Industry 4.0: Do They Need Each Other? Procedia Computer Science, Volume 200, 2022, pp. 140-150.
  7. Liu S., Wang Q., Luo Y., A review of applications of visual inspection technology based on image processing in the railway industry, Transportation Safety and Environment, Volume 1 (2019), Issue 3, pp. 185–204.
  8. Grudin J., Jacques R., Chatbots, Humbots, and the Quest for Artificial General Intelligence, Conference on Human Factors in Computing Systems – New York, Association for Computing Machinery (2019), Paper 209, pp. 1–11.
  9. Sellat Q., Bisoy S.K., Priyadarshini R., Chapter 10 - Semantic segmentation for self-driving cars using deep learning: a survey, Cognitive Big Data Intelligence with a Metaheuristic Approach, Academic Press (2022), pp. 211-238.
  10. Tang R., De Donato L., Bes̆ inović N., Flammini F., Goverde R., Lin Z., Liu R., Tang T., Vittorini V., Wang Z., A literature review of Artificial Intelligence applications in railway systems, Transportation Research Part C: Emerging Technologies, Vol. 140, (2022), 103679.
  11. Ghofrani F., He Q., Goverde R.M.P., Liu X. Recent applications of big data analytics in railway transportation systems: A survey, Transp. Res. C, 90 (2018), pp. 226-246.
  12. Xie, P., Li, T., Liu, J., Du, S., Yang, X., Zhang, J. Urban flow prediction from spatiotemporal data using machine learning: A survey, Inf. Fusion, 59 (2020), pp. 1-12.
  13. Jan, Z., Ahamed, F., Mayer, W., Patel, N., Grossmann, G., Stumptner, M.; Kuusk, A. Artificial Intelligence for Industry 4.0: Systematic Review of Applications, Challenges, and Opportunities. Expert Syst. Appl. (2022), 216, 119456.
  14. World Bank Group: What is Climate Change? https://climateknowledgeportal.worldbank.org/overview
  15. European Commission: Climate Action > Consequences of climate change. https://climate.ec.europa.eu/climate-change/consequences-climate-change_en
  16. Hawkins, Ed. National Centre for Atmospheric Science, University of Reading, UK. https://showyourstripes.info/s/europe/bulgaria/all
  17. Kim, B.M., S.W. Son, S.K. Min, J.H. Jeong, S.J. Kim, X.D. Zhang, T. Shim, J.H. Yoon. Weakening of the stratospheric polar vortex by Arctic sea-ice loss. // Nature Communications 5, 2014. DOI: 10.1038/Ncomms5646.
  18. POWER Data Access Viewer. https://power.larc.nasa.gov/data-access-viewer
  19. PVGIS. 2017, Photovoltaic Geographical Information System. http://re.jrc.ec.europa.eu/pvg_tools/en/tools.html
  20. National Institute of Meteorology and Hydrology. http://meteo.bg/
  21. Ivanova, S., Ivanova, E. Development of an approach for parametric metasynthesis of operational indicators of roads from the republican road network of Bulgaria based on satellite data. In Proceedings of the Sixth International Scientific Conference "Industry 4.0", 08-11.12.2021, Borovets resort complex, pp. 209-212.

Article full text

Download PDF