Effectiveness of Machine Learning in environmental pollution from remote sensing images. Systematic Literature Review

Dios-Castillo C.; Chafloque-Llontop C.; Sánchez-Rentería R. · Proceedings of the LACCEI international Multi-conference for Engineering, Education and Technology (2024) · DOI: 10.18687/laccei2024.1.1.1402

Resumen

Machine learning algorithms for the analysis of remote sensing images make it possible to identify and track sources of environmental pollution effectively. The objective or purpose of this research was to evaluate the effectiveness of these techniques in the identification and monitoring of sources of environmental pollution through remote sensing images. The systematic review of scientific articles on the topic in question was proposed as a methodology, with the support of the PICO questions and the PRISMA procedure for the analysis of these articles. The information was obtained from the Scopus database, obtaining 58 documents of open access and from the last 5 years. The results found show that machine learning algorithms allow a more accurate identification of sources of environmental pollution compared to conventional methods. It is concluded that these techniques are a valuable tool for the identification and monitoring of sources of environmental pollution, which can contribute to decision-making and the implementation of measures to reduce environmental pollution.

Instituciones: Universidad Tecnológica del Perú