AI‑POWERED DISSOLVED OXYGEN MONITORING AND PREDICTION

Authors

  • K. Shekhar, Ravula Sai Sreeja, Murahari Jaya Krishna, Guvva Nikesh Goud, Guduri Teja Sri

Keywords:

Keywords: Dissolved oxygen, river health, machine learning, water quality, prediction model, environmental monitoring.

Abstract

Dissolved oxygen (DO) is a key indicator of river ecosystem health, essential for the survival andrespiration of aquatic organisms. Traditional DO monitoring involves collecting water samples andanalyzing them in laboratories, which is time-consuming, expensive, and limited in both spatial andtemporal coverage. These limitations hinder real-time monitoring and accurate prediction, making it
challenging to maintain optimal DO levels. In response to these challenges approach to predict

References

Rizal, Nur Najwa Mohd, Gasim Hayder, and Salman Yussof. "River Water Quality Prediction and Analysis–Deep Learning Predictive Models Approach." Sustainability Challenges and Delivering Practical Engineering Solutions: Resources, Materials, Energy, and Buildings. Cham: Springer International Publishing, 2023. 25-29.

Zheng, Hang, et al. "Large-scale prediction of stream water quality using an interpretable deep learning approach." Journal of environmental management 331 (2023): 117309.

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Published

2025-04-02

How to Cite

K. Shekhar, Ravula Sai Sreeja, Murahari Jaya Krishna, Guvva Nikesh Goud, Guduri Teja Sri. (2025). AI‑POWERED DISSOLVED OXYGEN MONITORING AND PREDICTION. Journal of Computational Analysis and Applications (JoCAAA), 34(4), 359–372. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/2304

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