Abhishek Kumar – författare

Artificial Intelligence for Renewable Energy Systems addresses the energy industries remarkable move from traditional power generation to a cost-effective renewable energy system, and most importantly, the paradigm shift from a market-based cost of the commodity to market-based technological advancements. Featuring recent developments and state-of-the-art applications of artificial intelligence in renewable energy systems design, the book emphasizes how AI supports effective prediction for energy generation, electric grid related line loss prediction, load forecasting, and for predicting equipment failure prevention.

Looking at approaches in system modeling and performance prediction of renewable energy systems, this volume covers power generation systems, building service systems and combustion processes, exploring advances in machine learning, artificial neural networks, fuzzy logic, genetic algorithms and hybrid mechanisms.

Includes real-time applications that illustrates artificial intelligence and machine learning for various renewable systems Features a templated approach that can be used to explore results, with scientific implications followed by detailed case studies Covers computational capabilities and varieties for renewable system design

Handbook on New Paradigms in Smart Charging for E-Mobility: Global Trends, Policies and Practices provides a complete package for understanding and developing smart chargers for e-mobility applications. It discusses various concepts required for developing charging infrastructure and usage of different kinds of storage technologies, power electronics converters, controllers, communication requirements, grid infrastructure, sustainable technologies, policy frameworks, and all other related crucial aspects of E-mobility.Each part of the book covers a subdomain of e-mobility, beginning with an introductory chapter reviewing existing literature; the subsequent chapters are arranged to each follow the previous one. Other available books focus on specific technical subdomains of e-mobility, but none provides the wider outlook to meet the requirements of all audiences. This book uniquely brings together topics that are not otherwise easily accessible or available to these audiences.

This book will be beneficial for engineers, scientists, and researchers, providing them with a comprehensive standard benchmark work to explore the evolving aspects of charging infrastructure for e-mobility. Further, it will also help policymakers, practitioners and government entities formulate policies for successful implementations of e-motility for their masses. The techno-socio-economic focus will serve as standard literature for all.

Takes a modular approach, with each module catering to a different sub-domain of e-mobilityIncludes standalone chapters that cover out-of-the-box work related to e-mobilityPresents the latest advances and detailed technical descriptions of smart charging infrastructures

Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence covers computer-aided artificial intelligence and machine learning technologies as related to the impacts of climate change and its potential to prevent/remediate the effects. As such, different types of algorithms, mathematical relations and software models may help us to understand our current reality, predict future weather events and create new products and services to minimize human impact, chances of improving and saving lives and creating a healthier world.

This book covers different types of tools for the prediction of climate change and alternative systems which can reduce the levels of threats observed by climate change scientists. Moreover, the book will help to achieve at least one of 17 sustainable development goals i.e., climate action.

Includes case studies on the application of AI and machine learning for monitoring climate change effects and management Features applications of software and algorithms for modeling and forecasting climate change Shows how real-time monitoring of specific factors (temperature, level of greenhouse gases, rain fall patterns, etc.) are responsible for climate change and possible mitigation efforts to achieve environmental sustainability

Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them.Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector.The features of this book include: A unique and complete focus on applications of machine learning in the healthcare sector. An examination of how data analysis can be done using healthcare data and bioinformatics. An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors.

Machine Learning for Healthcare: Handling and Managing Data provides in-depth information about handling and managing healthcare data through machine learning methods. This book expresses the long-standing challenges in healthcare informatics and provides rational explanations of how to deal with them.Machine Learning for Healthcare: Handling and Managing Data provides techniques on how to apply machine learning within your organization and evaluate the efficacy, suitability, and efficiency of machine learning applications. These are illustrated in a case study which examines how chronic disease is being redefined through patient-led data learning and the Internet of Things. This text offers a guided tour of machine learning algorithms, architecture design, and applications of learning in healthcare. Readers will discover the ethical implications of machine learning in healthcare and the future of machine learning in population and patient health optimization. This book can also help assist in the creation of a machine learning model, performance evaluation, and the operationalization of its outcomes within organizations. It may appeal to computer science/information technology professionals and researchers working in the area of machine learning, and is especially applicable to the healthcare sector.The features of this book include: A unique and complete focus on applications of machine learning in the healthcare sector. An examination of how data analysis can be done using healthcare data and bioinformatics. An investigation of how healthcare companies can leverage the tapestry of big data to discover new business values. An exploration of the concepts of machine learning, along with recent research developments in healthcare sectors.

In the last two decades, machine learning has developed dramatically and is still experiencing a fast and everlasting change in paradigms, methodology, applications and other aspects. This book offers a compendium of current and emerging machine learning paradigms in healthcare informatics and reflects on their diversity and complexity. Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research. It provides many case studies and a panoramic view of data and machine learning techniques, providing the opportunity for novel insights and discoveries. The book explores the theory and practical applications in healthcare and includes a guided tour of machine learning algorithms, architecture design and interdisciplinary challenges.This book is useful for research scholars and students involved in critical condition analysis and computation models.

In the last two decades, machine learning has developed dramatically and is still experiencing a fast and everlasting change in paradigms, methodology, applications and other aspects. This book offers a compendium of current and emerging machine learning paradigms in healthcare informatics and reflects on their diversity and complexity. Machine Learning Approaches and Applications in Applied Intelligence for Healthcare Data Analytics presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research. It provides many case studies and a panoramic view of data and machine learning techniques, providing the opportunity for novel insights and discoveries. The book explores the theory and practical applications in healthcare and includes a guided tour of machine learning algorithms, architecture design and interdisciplinary challenges.This book is useful for research scholars and students involved in critical condition analysis and computation models.

Computer Vision and Machine Intelligence for Renewable Energy Systems offers a practical, systemic guide to the use of computer vision as an innovative tool to support renewable energy integration.This book equips readers with a variety of essential tools and applications: Part I outlines the fundamentals of computer vision and its unique benefits in renewable energy system models compared to traditional machine intelligence: minimal computing power needs, speed, and accuracy even with partial data. Part II breaks down specific techniques, including those for predictive modeling, performance prediction, market models, and mitigation measures. Part III offers case studies and applications to a wide range of renewable energy sources, and finally the future possibilities of the technology are considered. The very first book in Elsevier’s cutting-edge new series Advances in Intelligent Energy Systems, Computer Vision and Machine Intelligence for Renewable Energy Systems provides engineers and renewable energy researchers with a holistic, clear introduction to this promising strategy for control and reliability in renewable energy grids.Provides a sorely needed primer on the opportunities of computer vision techniques for renewable energy systemsBuilds knowledge and tools in a systematic manner, from fundamentals to advanced applicationsIncludes dedicated chapters with case studies and applications for each sustainable energy source

Computational Automation for Water Security: Enhancing Water Quality Management is a comprehensive and insightful guide which explores the challenges posed by inefficient and outdated practices, presenting innovative solutions to enhance decision-making, optimizing water treatment processes, and ultimately improving environmental outcomes. Through the coverage of advanced computational techniques, such as data analysis, machine learning, and optimization strategies, readers will gain a deep understanding of how computational automation can revolutionize decision-making. This book is an invaluable resource for professionals, researchers, and policymakers seeking to stay at the forefront of water quality management practices, harnessing the power of computational automation for a cleaner, healthier future.Offers a holistic understanding of the application of computational automation in water quality managementContains practical and unique updates to help learners how to apply computational techniques to address water quality challengesProvides a comprehensive and multidisciplinary perspective on water quality management

GANs in Medical Diagnostics and Classification explores the role of Generative Adversarial Networks (GANs) in addressing underdiagnosed and overlooked health challenges. The book bridges cutting-edge AI research with real-world medical applications, presenting interdisciplinary insights into how GANs enhance diagnostics, treatment precision, and disease surveillance across domains such as antimicrobial resistance, rare genetic disorders, and environmental health risks. It also examines ethical, policy, and accessibility dimensions of AI in healthcare. By combining technical depth with practical case studies, this volume equips medical researchers, AI engineers, and healthcare professionals with actionable knowledge to tackle silent epidemics using GAN-based tools.Delivers real-world case studies that demonstrate GAN applications in diagnosing rare diseases, detecting antimicrobial resistance, and enhancing medical imagingIntegrates interdisciplinary perspectives from medicine, AI, ethics, and policy to provide a holistic view of AI-driven healthcare innovationExplores emerging technologies such as synthetic data generation, wearable diagnostics, and AI-enhanced telemedicine for future-ready healthcare solutions

Explainable AI for Transparent and Trustworthy Medical Decision Support equips readers with a comprehensive and timely resource that presents the principles, methodologies, and real-world applications of explainable AI (XAI) within the medical context. Covering a wide range of use cases-from radiology and pathology to genomics and clinical decision support systems-the book provides in-depth discussions on how XAI techniques can enhance interpretability, improve clinician trust, meet regulatory requirements, and ultimately lead to better patient outcomes. The book demystifies the workings of machine learning models and highlights techniques that make them interpretable.It is designed to empower not only AI researchers and developers but also healthcare administrators and policymakers with the knowledge needed to evaluate, adopt, and trust AI solutions in critical medical applications. The book's authors bring together theory, implementation strategies, ethical implications, and case studies under one cover, offering a multidisciplinary perspective that aligns computer science with medical practice and healthcare policy.Presents detailed coverage of XAI methods such as SHAP, LIME, and Grad-CAM applied to medical dataProvides numerous case studies in diagnostics, ICU prediction, and radiology using explainable modelsIncludes discussions on ethics, bias, and regulatory frameworks such as GDPR and HIPAA

This book focusses on planning to practice aspects of microgrids. It covers basics, power electronics converters topologies, storage systems technologies, and control aspects. It further discusses control algorithms for sizing, scheduling, operation, and control, energy management and control architecture followed by power quality, reliability, stability, and conditioning issues. Operation and control, communication architectures and protocols, cybersecurity, and infrastructure requirements for IoT integration are included as well.Features:Provides comprehensive discussion on microgrid planning including detailed socio-policy aspects.Includes rich aspects of microgrid in planning, operation, and control.Covers concepts like E-mobility and communication protocols, cyber security aspects, and smart metering.Discusses power converters and storage system for microgrid applications.Explores real-time design standards, energy management models, forecasting models, stability, and power quality aspects of microgrids.This book is aimed at researchers, professionals, and graduate students in power engineering/electronics, renewable energy integration, and distributed generation.

This book provides scientific and technological insights on novel techniques of design and manufacturing using laser technologies. It showcases applications of laser micromachining in the biomedical industry, laser-based manufacturing processes in aerospace engineering, and high-precision laser-cutting in the home appliance sector. Features: Each chapter discusses a specific engineering problem and showcases its numerical, and experimental solution Provides scientific and technological insights on novel routes of design and manufacturing using laser technologies Synergizes exploration related to the various properties and functionalities through extensive theoretical and numerical modeling Highlights current issues, developments, and constraints in additive manufacturing Discusses applications of laser cutting machines in the manufacturing industry and laser micromachining for the biomedical industryThe text discusses optical, and laser-based green manufacturing technologies and their application in diverse engineering fields including mechanical, electrical, biomedical, and computer. It further covers sustainability issues in laser-based manufacturing technologies and the development of laser-based ultra-precision manufacturing techniques. The text also discusses the use of artificial intelligence and machine learning in laser-based manufacturing techniques. It will serve as an ideal reference text for senior undergraduate, graduate students, and researchers in fields including mechanical engineering, aerospace engineering, manufacturing engineering, and production engineering.

This book provides scientific and technological insights on novel techniques of design and manufacturing using laser technologies. It showcases applications of laser micromachining in the biomedical industry, laser-based manufacturing processes in aerospace engineering, and high-precision laser-cutting in the home appliance sector. Features: Each chapter discusses a specific engineering problem and showcases its numerical, and experimental solution Provides scientific and technological insights on novel routes of design and manufacturing using laser technologies Synergizes exploration related to the various properties and functionalities through extensive theoretical and numerical modeling Highlights current issues, developments, and constraints in additive manufacturing Discusses applications of laser cutting machines in the manufacturing industry and laser micromachining for the biomedical industryThe text discusses optical, and laser-based green manufacturing technologies and their application in diverse engineering fields including mechanical, electrical, biomedical, and computer. It further covers sustainability issues in laser-based manufacturing technologies and the development of laser-based ultra-precision manufacturing techniques. The text also discusses the use of artificial intelligence and machine learning in laser-based manufacturing techniques. It will serve as an ideal reference text for senior undergraduate, graduate students, and researchers in fields including mechanical engineering, aerospace engineering, manufacturing engineering, and production engineering.

This book presents an in-depth analysis of the various nanotechnologies that have been developed and their potential application in enhanced oil recovery (EOR). It begins with an introduction to EOR, and the current state of the oil and gas industry followed by discussion of various nanoparticles used in EOR, including metal and metal oxide nanoparticles, carbon-based nanoparticles, and composite nanoparticles.FeaturesExplains the various mechanisms by which nanoparticles can enhance oil recovery, and the challenges and limitations of using nanotechnology in EOR.Provides a comprehensive analysis of how nanoparticles affect EOR, practical application, and simulation.Explores stepwise information about the utility of nanotechnology in EOR.Includes dedicated case study chapters to get an in-depth idea of EOR by using nanotechnology.Illustrates various essential parameters affecting the EOR.Discusses the challenges of nanotechnology-based EOR.This book is aimed at graduate students, researchers, and professionals in Petroleum and Chemical Engineering, and Nanotechnology.

This book begins by introducing bio-inspired data-driven computation techniques, discussing bio-inspired swarm models, and highlighting the development of interactive bio-inspired energy harvesting systems to drive transportation infrastructure. It further covers important topics such as efficient control systems for distributed and hybrid renewable energy sources, and smart energy management systems for developing intelligent systems. This book:Presents data-driven intelligent heuristics for improving and advancing environmental sustainability in both eco-cities and smart cities. Discusses various efficient control systems for distributed and hybrid renewable energy sources and enhance the scope of smart energy management systems for developing even intelligent systems. Showcases how distributed energy systems improve the data-driven robots in the Internet of Medical Things. Highlights practical approaches to optimize power generation, reduce costs through efficient energy, and reduce greenhouse gas emissions to the possible minimum. Covers bio-inspired swarm models, smart data-driven sensing to combat environmental issues, and futuristic data-driven enabled schemes in blockchain-fog-cloud assisted medical eny ecosystem.The text is primarily written for graduate students, and academic researchers in diverse fiergelds including electrical engineering, electronics and communications engineering, computer science and engineering, and environmental engineering.

This book presents an in-depth analysis of the various nanotechnologies that have been developed and their potential application in enhanced oil recovery (EOR). It begins with an introduction to EOR, and the current state of the oil and gas industry followed by discussion of various nanoparticles used in EOR, including metal and metal oxide nanoparticles, carbon-based nanoparticles, and composite nanoparticles.FeaturesExplains the various mechanisms by which nanoparticles can enhance oil recovery, and the challenges and limitations of using nanotechnology in EOR.Provides a comprehensive analysis of how nanoparticles affect EOR, practical application, and simulation.Explores stepwise information about the utility of nanotechnology in EOR.Includes dedicated case study chapters to get an in-depth idea of EOR by using nanotechnology.Illustrates various essential parameters affecting the EOR.Discusses the challenges of nanotechnology-based EOR.This book is aimed at graduate students, researchers, and professionals in Petroleum and Chemical Engineering, and Nanotechnology.

The text discusses the fundamentals of lubrication science and technology linking the science concepts to engineering practices. It further explores the performance characterization of lubrication systems by utilizing sophisticated experiments and tests and motivates the readers to develop their conclusions and reach solutions based on modern tools and techniques.This book:Presents the principles of surface and lubricant chemistry, and its implementation to devise engineering solutions for various application-based systems.Discusses viscosity index improvers, tribology of green lubricants, and biolubricants from non-edible oils.Highlights 2D nanomaterials lubricants, biogreases, hydrogel and lubricants for extreme temperature and pressure conditions. Explains lubrication for electrical, biomedical, automobile, marine, turbine and aerospace applications. Covers design considerations, formulations, and compositions of lubricants for high-temperature applications in diverse areas. Explores the simulation, computational, and empirical models to characterize, quantify and mitigate the adverse effects of friction.It is primarily written for senior undergraduate and graduate students, and academic researchers in the fields of mechanical engineering, production engineering, industrial engineering, aerospace engineering, and manufacturing engineering.

The objective of this book is to showcase recent solutions and discuss the opportunities that AI, blockchain, and even their combinations can present to solve the issue of Internet of Things (IoT) security. It delves into cuttingedge technologies and methodologies, illustrating how these innovations can fortify IoT ecosystems against security threats. The discussion includes a comprehensive analysis of AI techniques such as machine learning and deep learning, which can detect and respond to security breaches in real time. The role of blockchain in ensuring data integrity, transparency, and tamper- proof transactions is also thoroughly examined. Furthermore, this book will present solutions that will help analyze complex patterns in user data and ultimately improve productivity.

The objective of this book is to showcase recent solutions and discuss the opportunities that AI, blockchain, and even their combinations can present to solve the issue of Internet of Things (IoT) security. It delves into cuttingedge technologies and methodologies, illustrating how these innovations can fortify IoT ecosystems against security threats. The discussion includes a comprehensive analysis of AI techniques such as machine learning and deep learning, which can detect and respond to security breaches in real time. The role of blockchain in ensuring data integrity, transparency, and tamper- proof transactions is also thoroughly examined. Furthermore, this book will present solutions that will help analyze complex patterns in user data and ultimately improve productivity.

This book begins by introducing bio-inspired data-driven computation techniques, discussing bio-inspired swarm models, and highlighting the development of interactive bio-inspired energy harvesting systems to drive transportation infrastructure. It further covers important topics such as efficient control systems for distributed and hybrid renewable energy sources, and smart energy management systems for developing intelligent systems. This book:Presents data-driven intelligent heuristics for improving and advancing environmental sustainability in both eco-cities and smart cities. Discusses various efficient control systems for distributed and hybrid renewable energy sources and enhance the scope of smart energy management systems for developing even intelligent systems. Showcases how distributed energy systems improve the data-driven robots in the Internet of Medical Things. Highlights practical approaches to optimize power generation, reduce costs through efficient energy, and reduce greenhouse gas emissions to the possible minimum. Covers bio-inspired swarm models, smart data-driven sensing to combat environmental issues, and futuristic data-driven enabled schemes in blockchain-fog-cloud assisted medical eny ecosystem.The text is primarily written for graduate students, and academic researchers in diverse fiergelds including electrical engineering, electronics and communications engineering, computer science and engineering, and environmental engineering.

The objective of this publication AI Modernisation Techniques for Contemporary Trends is to offer a comprehensive and practical framework for engineers, technicians, and individuals interested in converting conventional electromechanical systems into intelligent, interconnected, and efficient smart systems. The rising demand for smart technologies necessitates the need of upgrading current systems to effectively cater to these expectations. This book will serve as an extensive resource that aims to facilitate the integration of ancient and contemporary technologies, providing readers with guidance during the whole process of conversion.

The text discusses the fundamentals of lubrication science and technology linking the science concepts to engineering practices. It further explores the performance characterization of lubrication systems by utilizing sophisticated experiments and tests and motivates the readers to develop their conclusions and reach solutions based on modern tools and techniques.This book:Presents the principles of surface and lubricant chemistry, and its implementation to devise engineering solutions for various application-based systems.Discusses viscosity index improvers, tribology of green lubricants, and biolubricants from non-edible oils.Highlights 2D nanomaterials lubricants, biogreases, hydrogel and lubricants for extreme temperature and pressure conditions. Explains lubrication for electrical, biomedical, automobile, marine, turbine and aerospace applications. Covers design considerations, formulations, and compositions of lubricants for high-temperature applications in diverse areas. Explores the simulation, computational, and empirical models to characterize, quantify and mitigate the adverse effects of friction.It is primarily written for senior undergraduate and graduate students, and academic researchers in the fields of mechanical engineering, production engineering, industrial engineering, aerospace engineering, and manufacturing engineering.

The application of artificial intelligence (AI) to the management and development of renewable energy sources has revolutionized the energy sector by enhancing efficiency, reliability, and scalability. With the capability to analyze weather patterns, solar radiation, and wind speeds, AI-driven technologies help ensure optimal utilization of such resources as solar panels and wind turbines. They also enable precise forecasting of energy production. Additionally, AI-powered systems can facilitate real-time monitoring and predictive maintenance of renewable energy infrastructure to reduce downtime and operational costs. With AI, the renewable energy industry can overcome challenges, maximize output, and contribute significantly to global efforts in combating climate change.Artificial Intelligence for Renewable Energy Systems focuses on using AI technology to advance green energy and its management. Starting with an overview of applying AI to renewable energy management, the book follows with such real-world as:Predictive analytics for wind turbine maintenanceIntelligent solar grid integrationVehicle-to-grid (V2G) technology for electric mobilityIntelligent hydrogen fuel cell control.It also features global trend research, methods for data-driven forecasting, and policymaking based on advanced data analytics and visualization.Taking a multidisciplinary approach, the book is a guide for academics, energy practitioners, engineers, and policymakers who are leveraging the constructive collaboration between AI and renewable energy technologies.

The text will serve as a valuable resource for researchers, engineers, and students interested in gaining a comprehensive understanding of photovoltaic materials, from fundamental principles to emerging trends and future directions in solar cell technology.• Discusses topics such as synthesis processes, characterization techniques, and photovoltaic device architectures.• Focuses on emerging materials and technologies and includes novel approaches aimed at enhancing solar cell efficiency and durability.• Covers all aspects of photovoltaic materials from fundamentals to advanced topics such as perovskite solar cells and nanomaterials.• Explores the fundamental properties of photovoltaic materials, techniques for synthesizing them, and detailed characterization methods.• Emphasizes photovoltaic materials for hybrid energy systems that integrate solar power with other renewable energy sources like wind and hydroelectricity.The text is primarily written for senior undergraduates, graduate students, and academic researchers in electrical engineering, electronics and communication engineering, materials science, chemical engineering, nanoscience, and nanotechnology.

Blockchain Technology in Water Management and New Energy Economy explores the transformative role of blockchain in addressing two of the most pressing global challenges: efficient water resource management and the transition to a sustainable energy future. As climate change, population growth, and urbanization exert increasing pressure on natural resources, traditional systems for managing water and energy are proving inadequate. This book presents blockchain as a powerful enabler of transparency, decentralization, and trust in resource governance, offering innovative solutions that can reshape these critical sectors. The book is divided into two major sections. The first focuses on water management, covering how blockchain can enhance monitoring, prevent water theft, support real-time quality assessments, and enable equitable distribution through smart contracts. It examines case studies where decentralized ledger technology has improved water conservation, optimized irrigation systems in agriculture, and facilitated peer-to-peer water trading.The second section delves into the new energy economy, highlighting blockchain’s potential in supporting renewable energy integration, smart grids, decentralized energy markets, and carbon credit tracking. It discusses how blockchain can empower consumers to become "prosumers" — both producing and consuming energy — while maintaining secure and traceable transactions. Additionally, the book explores cross-cutting themes such as data privacy, regulatory challenges, scalability, and the socio-economic impact of blockchain adoption. Through real-world examples, technical insights, and forward-looking perspectives, this book provides a comprehensive guide for researchers, policy makers, engineers, and sustainability advocates seeking to understand and apply blockchain technology for a greener and more resource-efficient future.

This edited volume presents a comprehensive exploration of modern techniques and methodologies for evaluating water quality and suitability. It bridges traditional experimental approaches with cutting-edge artificial intelligence and remote sensing tools, offering an innovative perspective on sustainable water resource management.It delves into multidisciplinary insights that combine environmental science, data analytics, and hydrological modeling. Key topics include experimental water quality analysis, GIS-based assessment, groundwater and surface water case studies, and AI applications in hydroinformatics. Readers will benefit from practical frameworks, real-world case studies, and research-driven solutions designed to address complex challenges in water suitability evaluation.This is an essential resource for researchers, academicians, environmental engineers, policymakers, and postgraduate students working in water resource management, environmental sustainability, and artificial intelligence applications. It serves as both a reference guide and an inspiration for professionals seeking to implement intelligent, data-driven, and sustainable approaches to water assessment and planning.