Solar powered integrated multi sensors to monitor inland lake water quality using statistical data fusion technique with Kalman filter DOI Creative Commons
E. B. Priyanka, S. Thangavel,

R. Mohanasundaram

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 24, 2024

This study proposes a data-driven statistical model using multi sensor fusion and Kalman filtering for real-time water quality assessment in lakes. A recursive estimation technique, the Filter, is employed to handle uncertainties enhance computational efficiency. The process integrates data from sensors monitoring parameters like chlorophyll concentration, surface elevation, temperature, precipitation, producing Markov features capture temporal transitions environmental dynamics. Data synchronization are achieved through KF methods, enabling adaptive management response fluctuations such as seasonal changes, precipitation (6-18%), evaporation rates (1.2-11.9 mm/day). Over 30-day evaluation period, accurately predicted concentrations, reaching 128

Language: Английский

A comprehensive review of remote sensing techniques for monitoring Ulva prolifera green tides DOI Creative Commons
Xiaomeng Geng, Huiru Li, Le Wang

et al.

Frontiers in Marine Science, Journal Year: 2025, Volume and Issue: 12

Published: Jan. 28, 2025

In recent years, Ulva prolifera green tide, as a large-scale marine ecological phenomenon, has occurred frequently in coastal areas such the Yellow Sea and East China Sea, significantly affecting ecosystems fishery resources. With continuous advancement of remote sensing technologies, these technologies have become indispensable tools for monitoring tides. This review provides comprehensive overview advances band indices detecting tides, including spatiotemporal distribution analysis, area biomass estimation, drift trajectory modeling, investigations their driving mechanisms. Additionally, it identifies limitations unresolved challenges current approaches, constraints on data resolution, algorithmic biases, environmental variability. The potential integrating multi-source with parameters deep learning techniques is discussed, emphasizing roles improving accuracy reliability predicting aims to guide future research efforts technological innovations this field.

Language: Английский

Citations

1
Performance of ML-Based Classification Models as Edge Computing in IoT Nodes for a Marine Observatory DOI

Miguel Zaragoza-Esquerdo,

Vinie Lee Silva Alvarado, Lorena Parra

et al.

Learning and analytics in intelligent systems, Journal Year: 2025, Volume and Issue: unknown, P. 87 - 98

Published: Jan. 1, 2025

Language: Английский

Citations

0
Solar powered integrated multi sensors to monitor inland lake water quality using statistical data fusion technique with Kalman filter DOI Creative Commons
E. B. Priyanka, S. Thangavel,

R. Mohanasundaram

et al.

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 24, 2024

This study proposes a data-driven statistical model using multi sensor fusion and Kalman filtering for real-time water quality assessment in lakes. A recursive estimation technique, the Filter, is employed to handle uncertainties enhance computational efficiency. The process integrates data from sensors monitoring parameters like chlorophyll concentration, surface elevation, temperature, precipitation, producing Markov features capture temporal transitions environmental dynamics. Data synchronization are achieved through KF methods, enabling adaptive management response fluctuations such as seasonal changes, precipitation (6-18%), evaporation rates (1.2-11.9 mm/day). Over 30-day evaluation period, accurately predicted concentrations, reaching 128

Language: Английский

Citations

1