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Efficient Water Quality Monitoring

Paper Authors:

Samuel Yanes Luis,

Dmitriy Shutin,

Juan Marchal Gómez,

Daniel Gutiérrez Reina,

Sergio Toral Marín

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Key Details

Proposes system for water monitoring using autonomous surface vehicle fleet

Uses local Gaussian processes to model water quality data

Employs deep reinforcement learning to optimize vehicles' paths

Consensus algorithm enables safe, coordinated movement

Cut estimation error by 20-24% over other approaches

AI generated summary

Efficient Water Quality Monitoring

This paper proposes using a fleet of autonomous surface vehicles with sensors to efficiently monitor water quality over large areas. It combines local Gaussian processes to model the water quality data and deep reinforcement learning to optimize the vehicles' paths for maximizing information gain. In simulations, this approach reduced estimation error by 20-24% compared to other methods.

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