Utilizing Deep Learning and the Internet of Things to Monitor the Health of Aquatic Ecosystems to Conserve Biodiversity
Most Cited Paper
in Fish Ecology and Management Studies
18 Months Post-Publication
Class of 2026
“Utilizing Deep Learning and the Internet of Things to Monitor the Health of Aquatic Ecosystems to Conserve Biodiversity”
A research intelligence initiative by Pepkio
pri.pepkio.com/top-papers/fish-ecology-and-management-studies/2026/
Topics
water quality monitoring · freshwater biodiversity · Internet of Things (IoT) · deep learning · Generalized Regression-based Neural Networks (G-RNN) · multi-variational polynomial regression · Real-time monitoring · N-O3-N (nitrate nitrogen) · BO-D5 (biological oxygen demand) · P-O4 (phosphate) · N-H3-N (ammonia nitrogen) · aquatic ecosystem health · Water quality parameters · urbanized river · pollutant detection · chemical and biological measurements · IoT-based water quality monitoring · anomaly detection in water parameters · field water testing validation
Share
Cite this ranking
Pepkio Research Index (PRI). Topics and Trends in Most Cited Fish Ecology and Management Studies Papers, Class of 2026. https://pri.pepkio.com/top-papers/fish-ecology-and-management-studies/2026. Accessed 2026-07-15. Zheng Su, Tinsley Li, Thematic Shifts in Early-High-Impact Cancer Genomics and Diagnostics Research: A Bibliometric and Semantic Analysis. bioRxiv 2026.07.04.736459; doi: https://doi.org/10.64898/2026.07.04.736459