An AI-driven hybrid random forest-LSTM framework for IoT-based groundwater level prediction and sustainable water management in Chennai, India
- DOI
- 10.3389/frai.2026.1958933
- Published
- 2026-10-01
- Container
- Frontiers in Artificial Intelligence
- Publisher
- Frontiers Media SA
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.3389/frai.2026.1958933,
title = {An AI-driven hybrid random forest-LSTM framework for IoT-based groundwater level prediction and sustainable water management in Chennai, India},
author = {S. Boomika and R. Sitharthan and R. Tanukasree and R. Senthil Kumar},
year = {2026},
journal = {Frontiers in Artificial Intelligence},
doi = {10.3389/frai.2026.1958933},
url = {https://doi.org/10.3389/frai.2026.1958933}
}RIS
TY - JOUR TI - An AI-driven hybrid random forest-LSTM framework for IoT-based groundwater level prediction and sustainable water management in Chennai, India AU - S. Boomika AU - R. Sitharthan AU - R. Tanukasree AU - R. Senthil Kumar PY - 2026 JO - Frontiers in Artificial Intelligence DO - 10.3389/frai.2026.1958933 UR - https://doi.org/10.3389/frai.2026.1958933 ER -
APA
Boomika, S., Sitharthan, R., Tanukasree, R., & Kumar, R. S. (2026). An AI-driven hybrid random forest-LSTM framework for IoT-based groundwater level prediction and sustainable water management in Chennai, India. Frontiers in Artificial Intelligence. https://doi.org/10.3389/frai.2026.1958933
Source records
- crossref · retrieved 2026-11-03T13:31:10.500Z