Artificial Intelligence, Automation and Analytical Chemistry
Buch, Englisch, 502 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1025 g
ISBN: 978-3-032-03048-1
Verlag: Springer
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Geowissenschaften Umweltwissenschaften Umweltüberwachung, Umweltanalytik, Umweltinformatik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
- Geowissenschaften Umweltwissenschaften Umweltmanagement, Umweltökonomie
- Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung
- Geowissenschaften Geologie Hydrologie, Hydrogeologie
- Naturwissenschaften Chemie Analytische Chemie Umweltchemie, Lebensmittelchemie
Weitere Infos & Material
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.




