A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction
- DOI
- 10.1016/j.scitotenv.2022.157220
- Published
- 2022-11
- Container
- Science of The Total Environment
- Publisher
- Elsevier BV
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1016/j.scitotenv.2022.157220,
title = {A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction},
author = {Sumudu Senanayake and Biswajeet Pradhan and Abdullah Alamri and Hyuck-Jin Park},
year = {2022},
journal = {Science of The Total Environment},
doi = {10.1016/j.scitotenv.2022.157220},
url = {https://doi.org/10.1016/j.scitotenv.2022.157220}
}RIS
TY - JOUR TI - A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction AU - Sumudu Senanayake AU - Biswajeet Pradhan AU - Abdullah Alamri AU - Hyuck-Jin Park PY - 2022 JO - Science of The Total Environment DO - 10.1016/j.scitotenv.2022.157220 UR - https://doi.org/10.1016/j.scitotenv.2022.157220 ER -
APA
Senanayake, S., Pradhan, B., Alamri, A., & Park, H. (2022). A new application of deep neural network (LSTM) and RUSLE models in soil erosion prediction. Science of The Total Environment. https://doi.org/10.1016/j.scitotenv.2022.157220
Source records
- crossref · retrieved 2026-09-27T03:10:32.791Z