Artificial intelligence, machine learning and GIS in environmental engineering: current trends

The use of computational tools, such as Artificial Intelligence, Machine Learning or Geographic Information Systems, has had a significant impact on the knowledge generated on environmental issues in recent years. The number of publications shown in this preliminary review of the IEEE Xplore Digital Library database shows its broad applicability and scientific relevance. Search filters and keywords such as water, air, soil, climate change, energy and waste were used. The data obtained was processed to visualize the proportion of use in key topics of environmental interest, giving scientific guidance towards the sites of greatest applicability, as well as towards areas that deserve to be reinforced. In this way, this work aims to promote the development of collaborative work solutions in the related areas of environmental and computational engineering.

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Publication Details

Journal
Revista Tecnología en Marcha
Published
2024-09-09
DOI
https://doi.org/10.18845/tm.v37i7.7304
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
0.00
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article

Artificial intelligence, machine learning and GIS in environmental engineering: current trends

Laura Hernández-Alpízar, José Andrés Gómez-Mejía, María Belén Argüello-Vega
Revista Tecnología en Marcha
Air Quality Monitoring and Forecasting
article

Artificial intelligence, machine learning and GIS in environmental engineering: current trends

Laura Hernández-Alpízar, José Andrés Gómez-Mejía, María Belén Argüello-Vega
article en

Abstract

The use of computational tools, such as Artificial Intelligence, Machine Learning or Geographic Information Systems, has had a significant impact on the knowledge generated on environmental issues in recent years. The number of publications shown in this preliminary review of the IEEE Xplore Digital Library database shows its broad applicability and scientific relevance. Search filters and keywords such as water, air, soil, climate change, energy and waste were used. The data obtained was processed to visualize the proportion of use in key topics of environmental interest, giving scientific guidance towards the sites of greatest applicability, as well as towards areas that deserve to be reinforced. In this way, this work aims to promote the development of collaborative work solutions in the related areas of environmental and computational engineering.

Revista Tecnología en Marcha
Openalex Percentile: Top 86%
Air Quality Monitoring and Forecasting
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