"The emergence of AI as an applicable tool in wastewater treatment and water resource management has opened immense opportunities in water research and environmental sciences. It has provided solutions to otherwise complex problems not easily resolved by deterministic physics or traditional approaches. This book aims at providing in-depth and comprehensive research on AI techniques and their applications in wastewater treatment and in the management of water resources"--
Artificial intelligence and the use of computational methods to extract information from data are providing adequate tools to monitor and predict water pollutants and water quality issues faster and more accurately. Smart sensors and machine learning models help detect and monitor dispersion and leakage of pollutants before they reach groundwater. With contributions from experts in academia and industries, who give a unified treatment of AI methods and their applications in water science, this book help governments, industries, and homeowners not only address water pollution problems more quickly and efficiently, but also gain better insight into the implementation of more effective remedial measures. FEATURES Provides cutting-edge AI applications in water sector. Highlights the environmental models used by experts in different countries. Discusses various types of models using AI and its tools for achieving sustainable development in water and groundwater. Includes case studies and recent research directions for environmental issues in water sector. Addresses future aspects and innovation in AI field related to watersustainability. This book will appeal to scientists, researchers, and undergraduate and graduate students majoring in environmental or computer science and industry professionals in water science and engineering, environmental management, and governmental sectors. It showcases artificial intelligence applications in detecting environmental issues, with an emphasis on the mitigation and conservation of water and underground resources.
The emergence of a plethora of water contaminants as a result of industrialization has introduced complexity to water treatment processes. Such complexity may not be easily resolved using deterministic approaches. Artificial intelligence (AI) has found relevance and applications in almost all sectors and academic disciplines, including water treatment and management. AI provides dependable solutions in the areas of optimization, suspect screening or forensics, classification, regression, and forecasting, all of which are relevant for water research and management. Artificial Intelligence Applications in Water Treatment and Water Resource Management explores the different AI techniques and their applications in wastewater treatment and water management. The book also considers the benefits, challenges, and opportunities for future research. Covering key topics such as water wastage, irrigation, and energy consumption, this premier reference source is ideal for computer scientists, industry professionals, researchers, academicians, scholars, practitioners, instructors, and students.
Water Resource Modeling and Computational Technologies, Seventh Edition provides the reader with a comprehensive overview of the applications that computational techniques have in various sectors of water resource engineering. The book explores applications of recent modeling and computational techniques in various sectors of water resource engineering, including hydroinformatics, irrigation engineering, climate change, hydrologic forecasting, floods, droughts, image processing, GIS, water quality, aquifer mapping, basin scale modeling, computational fluid dynamics, numerical modeling of surges and groundwater flow, river engineering, optimal reservoir operation, multipurpose projects, and water resource management. As such, this is a must read for hydrologists, civil engineers and water resource managers. Presents contributed chapters from global experts in the field of water resources from both a science and engineering perspective Includes case studies throughout, providing readers with an opportunity to understand how case specific challenges can help with computational techniques Provides basic concepts as well as a literature review on the application of computational techniques in various sectors of water resources
The textbook explorers the intersection of artificial intelligence (AI) and machine learning (ML) within water supply distribution systems offer comprehensive insights into cutting-edge applications. Covering fundamental concepts, these texts delve into the intricacies of data collection, preprocessing, and modeling specific to water networks. By utilizing AI and ML algorithms, this book elucidate how to optimize system performance, addressing challenges such as pressure management and leak detection. Decision support systems powered by AI play a pivotal role in forecasting demands and efficiently managing distribution networks. Through engaging case studies, readers gain valuable perspectives on real-world implementations, fostering a deeper understanding of the transformative potential of AI and ML in enhancing water supply infrastructure.
Artificial Intelligence and Data Science in Environmental Sensing provides state-of-the-art information on the inexpensive mass-produced sensors that are used as inputs to artificial intelligence systems. The book discusses the advances of AI and Machine Learning technologies in material design for environmental areas. It is an excellent resource for researchers and professionals who work in the field of data processing, artificial intelligence sensors and environmental applications. Presents tools, connections and proactive solutions to take sustainability programs to the next level Offers a practical guide for making students proficient in modern electronic data analysis and graphics Provides knowledge and background to develop specific platforms related to environmental sensing, including control water, air and soil quality, water and wastewater treatment, desalination, pollution mitigation/control, and resource management and recovery
