Smart Water Quality Monitoring - Artificial Intelligence, Automation and Analytical Chemistry

This book offers a comprehensive overview of the state-of-the-art techniques for monitoring water quality, leveraging artificial intelligence (AI), IoT technologies, and autonomous vehicles to offer groundbreaking approaches to environmental protection. With contributions from leading experts in electronic engineering and marine sciences, this book presents a multidisciplinary perspective on solving one of the most pressing issues facing our planet: ensuring clean and sustainable water resources.
The chapters cover key concepts such as AI-driven platforms for ecosystem surveillance; chemical analytics for detecting pollutants; and predictive models for assessing future water quality scenarios. Particular attention is given to autonomous systems for dynamic data collection, where readers will learn more about the potential and research challenges of autonomous vehicles equipped with physico-chemical sensors and vision cameras to collect real-time data.
Through empirical research and theoretical insights, this book invites readers to explore innovative methodologies that promise not only to enhance understanding of water ecosystems but also to revolutionize how to monitor them.
Aimed at scholars and professionals across disciplines such as environmental engineering, marine science, sustainability studies, and information technology with an interest in ecological preservation, the book offers invaluable insights into developing effective monitoring systems that can adapt to the challenges posed by global environmental change. It also serves as an essential resource for institutions seeking to equip their libraries with the latest scientific advancements in water ecosystem management.

Sommario

Data Science and Public Policies: Towards Water Security.- Sources and Effects of Water Contamination.: Characteristics and Ecological Implications.- Use of omics techniques for assessing water quality.- Hyperspectral Technology to monitor marine pollution.- Machine and Deep Learning Approaches for Water Pollution Detection using Hyperspectral Imaging.- Intelligent Real-Time Anomaly Detection for Optimisation of Water Monitoring Systems.- Smart sensors for water quality monitoring in aquaculture systems.- Drought Impacts on Hydrological Ecosystem Services: Indicators and methodological processes.- Miniaturized (Bio)sensors for Aquatic Environmental Monitoring.- Autonomous Surface Vehicle (ASV) for Water Monitoring-using Artificial Intelligence Methodologies.- From Concept to Control: Development of an Advanced ASV Platform for Testing.- Model-Based Online Planning for Environmental Disaster Scenarios with Autonomous Vehicles.- Unmanned Underactuated Surface Vehicle Formation Control using Deep Reinforcement Learning.- MultiTask Multiagent Deep Reinforcement Learning for a fleet of Autonomous Surface Vehicles in Environmental Cleanup Missions.- Deep reinforcement learning and informative path planning: diving into the cooperation of heterogeneous aquatic surface vehicles.

Info autore

Daniel Gutiérrez is a Full Professor at the Electronic Engineering Department of University of Seville, Spain, and his current research interests include the application of meta-heuristic, machine learning, and deep algorithms to solve water monitoring problems using autonomous systems. With a M.S. degree in Electronics and Telecommunications, and a Ph.D. in Electronic Engineering from the University of Seville, Seville, Spain, Dr. Gutiérrez was previously a Visitor Researcher with Liverpool John Moores University, U.K., the Free University of Berlin, Germany, the Colorado School of Mines, USA, and Leeds Beckett University, U.K. He was also an Assistant Professor with Loyola University, Spain, from October 2018 to April 2019. He has published 60+ JCR publications in the field and contributes as editorial board of several international journals. 
Pablo Millán is the Director of the School of Engineering of Universidad Loyola Andalucía, Spain, and co-lead of the Optimization and Control of Distributed Systems (ODS) Research Group. Dr. Millán is a leading expert in intelligent systems and sustainable technologies, with over 60 research publications and leadership in major national and European R&D projects, including pioneering initiatives like AQUATRONC and ECOPORT, which advanced the control of autonomous marine vehicle fleets, and IRRIGATE, funded by the 2019 Andalusian R&D Call, which deals with the application of control and AI in smart agriculture. His international collaborations with institutions like KTH (Sweden), CNRS-LAAS (France), and Marquette University (USA) have enriched his global perspective on sustainable technology solutions, and his work has not only shaped academic discourse but also empowered communities—from Andalusia to Paraguay—through intelligent, scalable technologies.
Julián Blasco is a Research Professor and he was Director of the Institute for Marine Sciences of Andalusia from the Spanish Research Council (ICMAN-CSIC)from 2015–2023, and leader of the research group “Ecotoxicology, Ecophysiology and Biodiversity of Aquatic Systems”. He was President of the Iberoamerican Society of Environmental Contamination and Toxicology, and a member of the SETAC Europe Council. Prof. Blasco has been involved in 60+ research projects and 25+ research contracts at national and international levels, and has published 250+ scientific articles in peer-reviewed journals. He is co-editor in Chief of Marine Environmental Research journal and associate editor of several journals (e.g. Science of the Total Environment and Environmental Toxicology and Chemistry, among others). He has also contributed to several book chapters, and co-edited the book Sunscreens in Coastal Ecosystems: Occurrence, Behavior, Effect and Risk, and the textbook Marine Analytical Chemistry, published by Springer in 2020 and 2023, respectively.

Riassunto

This book offers a comprehensive overview of the state-of-the-art techniques for monitoring water quality, leveraging artificial intelligence (AI), IoT technologies, and autonomous vehicles to offer groundbreaking approaches to environmental protection. With contributions from leading experts in electronic engineering and marine sciences, this book presents a multidisciplinary perspective on solving one of the most pressing issues facing our planet: ensuring clean and sustainable water resources.
The chapters cover key concepts such as AI-driven platforms for ecosystem surveillance; chemical analytics for detecting pollutants; and predictive models for assessing future water quality scenarios. Particular attention is given to autonomous systems for dynamic data collection, where readers will learn more about the potential and research challenges of autonomous vehicles equipped with physico-chemical sensors and vision cameras to collect real-time data.
Through empirical research and theoretical insights, this book invites readers to explore innovative methodologies that promise not only to enhance understanding of water ecosystems but also to revolutionize how to monitor them.
Aimed at scholars and professionals across disciplines such as environmental engineering, marine science, sustainability studies, and information technology with an interest in ecological preservation, the book offers invaluable insights into developing effective monitoring systems that can adapt to the challenges posed by global environmental change. It also serves as an essential resource for institutions seeking to equip their libraries with the latest scientific advancements in water ecosystem management.

Dettagli sul prodotto

• Con la collaborazione di: Julian Blasco (Editore), Daniel Gutiérrez (Editore), Pablo Millán (Editore)
• Editore: Springer International Publishing
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 29.10.2025
• EAN: 9783032030481
• ISBN: 978-3-032-03048-1
• Pagine: 340
• Illustrazioni: X, 340 p. 66 illus., 1 illus. in color., farbige Illustrationen, schwarz-weiss Illustrationen
• Categorie: Scienze naturali, medicina, informatica, tecnica > Chimica > Altro; Umweltmanagement, Künstliche Intelligenz, Artificial Intelligence, Internet of things, Umweltüberwachung (Umwelt-Monitoring), Computernetzwerke und maschinelle Kommunikation, Hydrologie und die Hydrosphäre, Water, Environmental Management, Environmental Chemistry, Water quality, Environmental Monitoring, Deep Reinforcement Learning, Water Security, autonomous vehicles, automatic control, Unmanned robot-assisted hydrobiological monitoring, Water Analytics, Water Monitoring, Hyperspectral technology, Water Physical and Chemical analysis