{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T01:53:18Z","timestamp":1777513998930,"version":"3.51.4"},"reference-count":99,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T00:00:00Z","timestamp":1717545600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>This is a systematic literature review of the application of machine learning (ML) algorithms in geosciences, with a focus on environmental monitoring applications. ML algorithms, with their ability to analyze vast quantities of data, decipher complex relationships, and predict future events, and they offer promising capabilities to implement technologies based on more precise and reliable data processing. This review considers several vulnerable and particularly at-risk themes as landfills, mining activities, the protection of coastal dunes, illegal discharges into water bodies, and the pollution and degradation of soil and water matrices in large industrial complexes. These case studies about environmental monitoring provide an opportunity to better examine the impact of human activities on the environment, with a specific focus on water and soil matrices. The recent literature underscores the increasing importance of ML in these contexts, highlighting a preference for adapted classic models: random forest (RF) (the most widely used), decision trees (DTs), support vector machines (SVMs), artificial neural networks (ANNs), convolutional neural networks (CNNs), principal component analysis (PCA), and much more. In the field of environmental management, the following methodologies offer invaluable insights that can steer strategic planning and decision-making based on more accurate image classification, prediction models, object detection and recognition, map classification, data classification, and environmental variable predictions.<\/jats:p>","DOI":"10.3390\/make6020059","type":"journal-article","created":{"date-parts":[[2024,6,5]],"date-time":"2024-06-05T05:59:42Z","timestamp":1717567182000},"page":"1263-1280","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":45,"title":["Machine Learning in Geosciences: A Review of Complex Environmental Monitoring Applications"],"prefix":"10.3390","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3696-1589","authenticated-orcid":false,"given":"Maria Silvia","family":"Binetti","sequence":"first","affiliation":[{"name":"Water Research Institute, Italian National Research Council (IRSA-CNR), 70132 Bari, Italy"},{"name":"Department of Earth and Geoenvironmental Sciences, University of Bari Aldo Moro, 70125 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8006-6998","authenticated-orcid":false,"given":"Carmine","family":"Massarelli","sequence":"additional","affiliation":[{"name":"Water Research Institute, Italian National Research Council (IRSA-CNR), 70132 Bari, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1373-7055","authenticated-orcid":false,"given":"Vito Felice","family":"Uricchio","sequence":"additional","affiliation":[{"name":"Water Research Institute, Italian National Research Council (IRSA-CNR), 70132 Bari, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e2022JB026310","DOI":"10.1029\/2022JB026310","article-title":"Machine Learning Developments and Applications in Solid-Earth Geosciences: Fad or Future?","volume":"128","author":"Li","year":"2023","journal-title":"J. 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