Artificial Intelligence And Modeling For Water Sustainability

"With contributions from experts in academia and industries, who give a unified treatment of AI methods and their applications in water science, this book helps governments, industries, and homeowners not only address water pollution problems more quickly and efficiently, but also gain a better insight into the implementation of more effective remedial measures."--

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.

Hydrogen is poised to play a major role in the transition towards a net-zero economy. However, the worldwide implementation of hydrogen energy is restricted by several challenges, including those related to practical, easy, safe, and cost-effective storage and production methodologies. Nanomaterials present a promising solution, playing an integral role in overcoming the limitations of hydrogen production and storage. This book explores these innovations, covering a wide spectrum of applications of nanomaterials for sustainable hydrogen production and storage. Provides an overview of the hydrogen economy and its role in the transition to a net-zero economy. Details various nanomaterials for hydrogen production and storage as well as modeling and optimization of nanomaterials production. Features real-life case studies on innovations in nanomaterials applications for hydrogen storage. Discusses both the current status and future prospects. Aimed at researchers and professionals in chemical, materials, energy, environmental and related engineering disciplines, this work provides readers with an overview of the latest techniques and materials for the development and advancement of hydrogen energy technologies.

The world is addressing the insistent challenge of climate change, and the need for innovative solutions has become paramount. In this period of technical developments, artificial intelligence (AI) has emerged as a powerful instrument with enormous prospects to combat climate change and other environmental subjects. AI's ability to process vast amounts of data, identify patterns, and make intelligent predictions offers unprecedented opportunities to tackle this global crisis. High-Performance Computing (HPC) or super-computing environments address these large and complex challenges with individual nodes (computers) working together in a cluster (connected group) to perform massive amounts of computing in a short period. Creating and removing these clusters is often automated in the cloud to reduce costs. Computer networks, communication systems, and other IT infrastructures have a growing environmental footprint due to significant energy consumption and greenhouse gas emissions. To address this seemingly self-defeating conundrum, and create a truly sustainable environment, new energy models, algorithms, methodologies, platforms, tools, and systems are required to support next-generation computing and communication infrastructures. Harnessing High-Performance Computing and AI for Environmental Sustainability navigates through AI-driven solutions from sustainable agriculture and land management to energy optimization and smart grids. It unveils how AI algorithms can analyze colossal datasets, offering unprecedented insights into climate modeling, weather prediction, and long-term climate trends. Integrating AI-powered optimization algorithms revolutionizes energy systems, propelling the transition towards a low-carbon future by reducing greenhouse gas emissions and enhancing efficiency. This book is ideal for educators, environmentalists, industry professionals, and researchers alike, and it explores the ethical dimensions and policies surrounding AI's contribution to environmental development.

The complex and multidisciplinary nature of environmental problems requires that they are dealt with in an integrated manner. Modeling and software have become key instruments used to promote sustainability and improve environmental decision processes, especially through systematic integration of various knowledge and data and their ability to foster learning and help make predictions. This book presents the current state-of-the-art in environmental modeling and software and identifies the future challenges in the field. State-of-the-art in environmental modeling and software theory and practice for integrated assessment and management serves as a starting point for researchers Identifies the areas of research and practice required for advancing the requisite knowledge base and tools, and their wider usage Best practices of environmental modeling enables the reader to select appropriate software and gives the reader tools to integrate natural system dynamics with human dimensions

How can environmental scientists and engineers use the increasing amount of available data to enhance our understanding of planet Earth, its systems and processes? This book describes various potential approaches based on artificial intelligence (AI) techniques, including neural networks, decision trees, genetic algorithms and fuzzy logic. Part I contains a series of tutorials describing the methods and the important considerations in applying them. In Part II, many practical examples illustrate the power of these techniques on actual environmental problems. International experts bring to life ways to apply AI to problems in the environmental sciences. While one culture entwines ideas with a thread, another links them with a red line. Thus, a “red thread“ ties the book together, weaving a tapestry that pictures the ‘natural’ data-driven AI methods in the light of the more traditional modeling techniques, and demonstrating the power of these data-based methods.

“Data-Driven Modeling: Using MATLAB® in Water Resources and Environmental Engineering” provides a systematic account of major concepts and methodologies for data-driven models and presents a unified framework that makes the subject more accessible to and applicable for researchers and practitioners. It integrates important theories and applications of data-driven models and uses them to deal with a wide range of problems in the field of water resources and environmental engineering such as hydrological forecasting, flood analysis, water quality monitoring, regionalizing climatic data, and general function approximation. The book presents the statistical-based models including basic statistical analysis, nonparametric and logistic regression methods, time series analysis and modeling, and support vector machines. It also deals with the analysis and modeling based on artificial intelligence techniques including static and dynamic neural networks, statistical neural networks, fuzzy inference systems, and fuzzy regression. The book also discusses hybrid models as well as multi-model data fusion to wrap up the covered models and techniques. The source files of relatively simple and advanced programs demonstrating how to use the models are presented together with practical advice on how to best apply them. The programs, which have been developed using the MATLAB® unified platform, can be found on extras.springer.com. The main audience of this book includes graduate students in water resources engineering, environmental engineering, agricultural engineering, and natural resources engineering. This book may be adapted for use as a senior undergraduate and graduate textbook by focusing on selected topics. Alternatively, it may also be used as a valuable resource book for practicing engineers, consulting engineers, scientists and others involved in water resources and environmental engineering.

Water Engineering Modeling and Mathematic Tools provides an informative resource for practitioners who want to learn more about different techniques and models in water engineering and their practical applications and case studies. The book provides modelling theories in an easy-to-read format verified with on-site models for specific regions and scenarios. Users will find this to be a significant contribution to the development of mathematical tools, experimental techniques, and data-driven models that support modern-day water engineering applications. Civil engineers, industrialists, and water management experts should be familiar with advanced techniques that can be used to improve existing systems in water engineering. This book provides key ideas on recently developed machine learning methods and AI modelling. It will serve as a common platform for practitioners who need to become familiar with the latest developments of computational techniques in water engineering. Includes firsthand experience about artificial intelligence models, utilizing case studies Describes biological, physical and chemical techniques for the treatment of surface water, groundwater, sea water and rain/snow Presents the application of new instruments in water engineering