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October 19, 2025Environmental Science Water Research & Technology

Privacy-Preserving Water Quality Forecasting Using Federated Learning Across Distributed Water Monitoring Nodes and Optimized RPART Modelling

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Utilizing federated learning enhances water quality prediction accuracy while preserving data privacy.
Forecasting results from optimized RPART modelling show significant improvements in prediction efficiency.
Distributed monitoring nodes play a critical role in managing water quality by providing real-time data.
This privacy-preserving technique could revolutionize environmental management practices in polluted areas.

Implication

This approach demonstrates improved water quality prediction in distributed systems, highlighting privacy benefits.

Abstract

Water quality prediction is a highly important task in the anticipation and management of a polluted environment. An accurate prediction can help to assist the water quality environment to make...

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Cite This Study

Jenifel et al. (2025) studied this question.

synapsesocial.com/papers/68f43efb854d1061a58ac0ce https://doi.org/10.1039/d5ew00758e

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Implication

This approach demonstrates improved water quality prediction in distributed systems, highlighting privacy benefits.

Abstract

Water quality prediction is a highly important task in the anticipation and management of a polluted environment. An accurate prediction can help to assist the water quality environment to make...

Privacy-Preserving Water Quality Forecasting Using Federated Learning Across Distributed Water Monitoring Nodes and Optimized RPART Modelling

Key Points

Implication

Abstract

Discussion

Cite This Study

Privacy-Preserving Water Quality Forecasting Using Federated Learning Across Distributed Water Monitoring Nodes and Optimized RPART Modelling

Key Points

Implication

Abstract

Discussion

Cite This Study

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