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Water Science Volume 29, 2015 - Issue 1

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Review Article

Satellite data and real time stations to improve water quality of Lake ManzalahFootnote
Peer review under responsibility of National Water Research Center.

M.F. MohamedDrainage Research Institute, EgyptCorrespondence[email protected]

Pages 68-76 | Received 28 Oct 2014, Accepted 24 Mar 2015, Published online: 03 May 2019

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https://doi.org/10.1016/j.wsj.2015.03.002
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Citations (6)

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P. Durga Devi & G. Mamatha. (2024) Machine learning approach to predict the turbidity of Saki Lake, Telangana, India, using remote sensing data. Measurement: Sensors 33, pages 101139.
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Janjhyam Venkata Naga Ramesh, Pavithra Roy Patibandla, Manjula Shanbhog, Srinivas Ambala, Mohd Ashraf & Ajmeera Kiran. (2023) Ensemble Deep Learning Approach for Turbidity Prediction of Dooskal Lake Using Remote Sensing Data. Remote Sensing in Earth Systems Sciences 6:3-4, pages 146-155.
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Sabastian Simbarashe Mukonza & Jie-Lun Chiang. (2023) Meta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring. Environments 10:10, pages 170.
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Ana Andries, Stephen Morse, Richard J. Murphy, Jim Lynch & Emma R. Woolliams. (2022) Using Data from Earth Observation to Support Sustainable Development Indicators: An Analysis of the Literature and Challenges for the Future. Sustainability 14:3, pages 1191.
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Satya Prakash & Pinakana Sai Deepak. 2022. Addressing Environmental Challenges Through Spatial Planning. Addressing Environmental Challenges Through Spatial Planning 197 218 .

Kelly Antonini, Martin Langer, Ahmed Farid & Ulrich Walter. (2017) SWEET CubeSat – Water detection and water quality monitoring for the 21st century. Acta Astronautica 140, pages 10-17.
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