{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T19:04:50Z","timestamp":1772910290768,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,15]],"date-time":"2024-12-15T00:00:00Z","timestamp":1734220800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Health and Digital Executive Agency (HADEA)","award":["101091616"],"award-info":[{"award-number":["101091616"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Acid Mine Drainage (AMD) presents significant environmental challenges, particularly in regions with extensive mining activities. Effective monitoring and mapping of AMD are crucial for mitigating its detrimental impacts on ecosystems and water quality. This study investigates the application of Machine Learning (ML) algorithms to map AMD by fusing multispectral imagery from Sentinel-2 with high-resolution imagery from WorldView-3. We applied three widely used ML models\u2014Random Forest (RF), K-Nearest Neighbor (KNN), and Multilayer Perceptron (MLP)\u2014to address both classification and regression tasks. The classification models aimed to distinguish between AMD and non-AMD samples, while the regression models provided quantitative pH mapping. Our experiments were conducted on three lakes in the Outokumpu mining area in Finland, which are affected by mine waste and acidic drainage. Our results indicate that combining Sentinel-2 and WorldView-3 data significantly enhances the accuracy of AMD detection. This combined approach leverages the strengths of both datasets, providing a more robust and precise assessment of AMD impacts.<\/jats:p>","DOI":"10.3390\/rs16244680","type":"journal-article","created":{"date-parts":[[2024,12,16]],"date-time":"2024-12-16T10:08:53Z","timestamp":1734343733000},"page":"4680","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Machine Learning Algorithms for Acid Mine Drainage Mapping Using Sentinel-2 and Worldview-3"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7672-9346","authenticated-orcid":false,"given":"Fahimeh","family":"Farahnakian","sequence":"first","affiliation":[{"name":"Geological Survey of Finland (GTK), 02151 Espoo, Finland"},{"name":"Department of Computing, University of Turku, 20014 Turku, Finland"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2336-9148","authenticated-orcid":false,"given":"Nike","family":"Luodes","sequence":"additional","affiliation":[{"name":"Geological Survey of Finland (GTK), 02151 Espoo, Finland"}]},{"given":"Teemu","family":"Karlsson","sequence":"additional","affiliation":[{"name":"Geological Survey of Finland (GTK), 02151 Espoo, Finland"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1121","DOI":"10.1126\/science.167.3921.1121","article-title":"Acidic mine drainage: The rate-determining step","volume":"167","author":"Singer","year":"1970","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"897","DOI":"10.1111\/ejss.12160","article-title":"Mapping Contaminated Soils: Using Remotely-Sensed Hyperspectral Data to Predict pH","volume":"65","author":"Ong","year":"2014","journal-title":"Eur. 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