Machine Learning and Hyperparameters Algorithms for Identifying Groundwater Aflaj Potential Mapping in Semi-Arid Ecosystems Using LiDAR, Sentinel-2, GIS Data, and Analysis

Khalifa M. Al-Kindi, Saeid Janizadeh

Open source

DOI
10.3390/rs14215425
Published
2022-10-28
Container
Remote Sensing
Publisher
MDPI AG
Open access
unknown

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BibTeX

@article{allodium:10.3390/rs14215425,
  title = {Machine Learning and Hyperparameters Algorithms for Identifying Groundwater Aflaj Potential Mapping in Semi-Arid Ecosystems Using LiDAR, Sentinel-2, GIS Data, and Analysis},
  author = {Khalifa M. Al-Kindi and Saeid Janizadeh},
  year = {2022},
  journal = {Remote Sensing},
  doi = {10.3390/rs14215425},
  url = {https://doi.org/10.3390/rs14215425}
}

RIS

TY  - JOUR
TI  - Machine Learning and Hyperparameters Algorithms for Identifying Groundwater Aflaj Potential Mapping in Semi-Arid Ecosystems Using LiDAR, Sentinel-2, GIS Data, and Analysis
AU  - Khalifa M. Al-Kindi
AU  - Saeid Janizadeh
PY  - 2022
JO  - Remote Sensing
DO  - 10.3390/rs14215425
UR  - https://doi.org/10.3390/rs14215425
ER  - 

APA

Al-Kindi, K. M., & Janizadeh, S. (2022). Machine Learning and Hyperparameters Algorithms for Identifying Groundwater Aflaj Potential Mapping in Semi-Arid Ecosystems Using LiDAR, Sentinel-2, GIS Data, and Analysis. Remote Sensing. https://doi.org/10.3390/rs14215425

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