A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Machine Learning Algorithms for Acid Mine Drainage Mapping Using Sentinel-2 and Worldview-3
Tekijät: Farahnakian, Fahimeh; Luodes, Nike; Karlsson, Teemu
Kustantaja: MDPI
Kustannuspaikka: BASEL
Julkaisuvuosi: 2024
Journal: Remote Sensing
Tietokannassa oleva lehden nimi: REMOTE SENSING
Lehden akronyymi: REMOTE SENS-BASEL
Artikkelin numero: 4680
Vuosikerta: 16
Numero: 24
Sivujen määrä: 17
eISSN: 2072-4292
DOI: https://doi.org/10.3390/rs16244680
Verkko-osoite: https://doi.org/10.3390/rs16244680
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/478106500
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-Random Forest (RF), K-Nearest Neighbor (KNN), and Multilayer Perceptron (MLP)-to 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.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
This work is part of the Secure and Sustainable Supply of raw materials for EU Industry (S34I) project, n.101091616, funded by European Health and Digital Executive Agency (HADEA).
