Jara, M, Johnson, P, Flores, IJ and Gutiérrez, D (2023) Monitoring Peak Pollution Points of Water Resources with Autonomous Surface Vehicles using a PSO-based Informative Path Planner. In: Mobile Robots: Motion Control and Path Planning Studies in Computational Intelligence. Studies in Computational Intelligence, 1090 . Springer, pp. 93-125. ISBN 9783031265631

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Abstract

The preservation of water resources is an increasingly urgent issue. Therefore, monitoring the water quality of these resources is a very important task so that appropriate actions could be taken. This chapter focuses on water resource monitoring using a fleet of Autonomous Surface Vehicles equipped with sensors capable of measuring water quality parameters. The objective is to obtain the maximum points of contamination of the water resource through the exploration and exploitation of the water surface. The proposed algorithm is based on Particle Swarm Optimization (PSO) in combination with some machine learning techniques (Gaussian Process, Bayesian Optimization, among others) to address the limitations of PSO, such as premature convergence and difficulty in setting the initial values of the coefficients. To validate the performance of the algorithm, uni-modal and multi-modal benchmark functions are used in the simulation experiments. The results show that the proposed algorithm, the Enhanced GP-based PSO, based on the epsilon greedy method has the best performance for detecting water resource pollution peaks. It was also demonstrated that this algorithm is the one that generates the most accurate water quality model. However, when it comes to finding the highest pollution peak, the algorithm with the best response is the Enhanced GP-based PSO with a focus on exploitation.

Item Type:	Book Section
Uncontrolled Keywords:	Artificial Intelligence & Image Processing
Subjects:	T Technology > TD Environmental technology. Sanitary engineering T Technology > TJ Mechanical engineering and machinery
Divisions:	Engineering
Publisher:	Springer
SWORD Depositor:	A Symplectic
Date Deposited:	20 Sep 2023 14:35
Last Modified:	20 Sep 2023 14:35
DOI or ID number:	10.1007/978-3-031-26564-8_4
URI:	https://researchonline.ljmu.ac.uk/id/eprint/21523

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