Application of machine learning algorithms in geoscience: comprehensive review and future challenge
DOI:
https://doi.org/10.24054/raaas.v14i2.2783Keywords:
Application, Machine Learning, Geoscience, natural phenomenaAbstract
This article addresses the application of Machine Learning (ML) techniques in geoengineering and geoscience, highlighting its relevance in the prediction and understanding of natural phenomena. Despite the absence of specific physical laws, ML models offer flexibility to adapt and discover complex patterns. In particular, the ability of ML to improve the accuracy and efficiency in predicting susceptibility to land gradients is highlighted, with approaches such as supervised and unsupervised learning. The importance of understanding why a model classifies certain classes is mentioned, offering explainable tools that allow results that are aligned with the physical understanding of geological processes. Additionally, crucial applications of ML in geotechnical engineering are explored, with models based on algorithms such as support vector machines, artificial neural networks, and Bayes classifiers. The need to investigate the compatibility of physics-based models and AI data is highlighted for a more complete understanding and reliable predictions. The integration of ML techniques in geoengineering emerges as a key strategy to address current climate and anthropogenic challenges, offering new perspectives in the investigation of land sectors and other geological hazards. This article is part of the research carried out within the framework of the Master's Degree in Environmental Engineering, which seeks to explore the potential of Machine Learning for the management of geological risks.
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