Novel Ensemble Approaches of Machine Learning Techniques in Modeling the Gully Erosion Susceptibility
- DOI
- 10.3390/rs12111890
- Published
- 06
- Container
- Remote Sensing
- Publisher
- Not recorded
- Open access
- yes
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Cite this work
BibTeX
@article{allodium:10.3390/rs12111890,
title = {Novel Ensemble Approaches of Machine Learning Techniques in Modeling the Gully Erosion Susceptibility},
author = {Alireza Arabameri and Omid Asadi Nalivan and Sunil Saha and Jagabandhu Roy and Biswajeet Pradhan and John P. Tiefenbacher and Phuong Thao Thi Ngo},
year = {2020},
journal = {Remote Sensing},
doi = {10.3390/rs12111890},
url = {https://doi.org/10.3390/rs12111890}
}RIS
TY - JOUR TI - Novel Ensemble Approaches of Machine Learning Techniques in Modeling the Gully Erosion Susceptibility AU - Alireza Arabameri AU - Omid Asadi Nalivan AU - Sunil Saha AU - Jagabandhu Roy AU - Biswajeet Pradhan AU - John P. Tiefenbacher AU - Phuong Thao Thi Ngo PY - 2020 JO - Remote Sensing DO - 10.3390/rs12111890 UR - https://doi.org/10.3390/rs12111890 ER -
APA
Arabameri, A., Nalivan, O. A., Saha, S., Roy, J., Pradhan, B., Tiefenbacher, J. P., & Ngo, P. T. T. (2020). Novel Ensemble Approaches of Machine Learning Techniques in Modeling the Gully Erosion Susceptibility. Remote Sensing. https://doi.org/10.3390/rs12111890
Source records
- doaj · retrieved 2026-09-25T10:42:46.120Z