An improved extreme learning machine algorithm for prospectivity mapping of copper deposits using multi-source remote sensing data: a case study in the North Altyn Tagh, Xinjiang, China
Traditional extreme learning machine (ELM) model suffers from instability due to random initialization of input weights and hidden-layer bias, often resulting in suboptimal predictive performance. To address this limitation, the Slime Mould Algorithm (SMA), a bio-inspired optimization strategy, was...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-08-01
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| Series: | International Journal of Digital Earth |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/17538947.2025.2510567 |
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