Machine Learning Predictive Models for Phosphate Exploration-Reference-Cited by-同舟云学术

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Machine Learning Predictive Models for Phosphate Exploration

Published:2023-07-16 Issue: Volume: Page:
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Container-title:IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium
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Author:

Mezned Nouha¹

Affiliation:

1. University of Carthage,Faculty of Sciences of Bizerte,Tunisia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10281394/10281399/10282441.pdf?arnumber=10282441

Reference13 articles.

1. Hyperspectral Remote Sensing of Mine Wastes Impact in the North of Tunisia;alayet;Phd thesis Faculty of Sciences of Tunis,0

2. Spectral Modeling for Mine Tailing Mapping Based on Remote Sensing Data in the North of Tunisia;mezned;Phd thesis Faculty of Sciences of Tunis,0

3. PLS-regression: a basic tool of chemometrics

4. Support vector machine for multi-classification of mineral prospectivity areas

5. Optical Remote Sensing for Fluorapatite Content Estimation;mezned;Minerals Intech Open,2022

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Hyperspectral Signatures of Minerals From the TUNL Library for Geological Mapping: Clays, Sulfur and Iron Minerals;IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium;2024-07-07

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