Integrated Smart Technologies for Sustainable Water Systems Using Computer Applications (Smart Technologies for Water Engineering & Sustainability)

Integrated Smart Technologies for Sustainable Water Systems Using Computer Applications presents a comprehensive exploration of how cutting-edge computational technologies are revolutionizing water resource management. This multidisciplinary guide demonstrates the practical integration of artificial intelligence (AI), Internet of Things (IoT) systems, and emerging digital technologies to address critical challenges in hydrology, hydraulics, water quality, and smart infrastructure development.

This book systematically examines the application of advanced technologies including AI, machine learning, IoT, blockchain, cloud and edge computing, and Geographic Information Systems across diverse water management scenarios. Through theoretical models, algorithmic approaches, and real-world case studies from both developed and developing countries, it illustrates key findings in AI-driven water demand and quality predictions, IoT-enabled real-time distribution network monitoring, blockchain applications for transparent water resource transactions, intelligent flood forecasting systems, automated irrigation methods, wastewater treatment automation, and digital twin development for dynamic infrastructure modeling. The work uniquely incorporates emerging concepts such as explainable AI, climate resilience frameworks, and digital twin technologies, positioning it at the forefront of current industry and academic research trends.

This essential resource is designed for researchers, industry professionals, software developers, educators, and students specializing in water resource management and sustainable technology applications. The content balances foundational insights with advanced methodologies, making it equally valuable for newcomers seeking a comprehensive understanding and experienced practitioners looking to implement cutting-edge solutions for sustainable water system management.

Srikanta Kumar Mohapatra is currently serving as a professor in the Department of Computer Science and Engineering at Chitkara University, India. He has more than 18 years of combined teaching and industrial experience. He has published over 70 research articles in reputed national and international journals and conferences and served as a member of editorial and reviewer boards for leading publishing houses. He has authored three reference books and co-edited 1 book. He has filed/published more than 20 patents, out of which 15 patents have been granted. Recently, he received the CSE Excellence Award from Chitkara University in recognition of his significant contributions to patents and the CRISP initiative. His research interests include Artificial Intelligence, Machine Learning, Blockchain, Cloud Computing, and Computational Nanotechnology.

Monika Sethi is currently serving as a professor in the Department of Computer Science and Engineering at Chitkara University, India. Her research interests focus on image processing (ADNI-MRI Images), deep learning, machine learning, and wireless sensor networks. Her teaching interests include C, C++, Python, data structures, operating systems, computer system architecture, computer graphics and digital electronics. She has published 20+ research papers in international journals and conferences (Scopus Indexed) and filed 14 patents (9 granted and 5 published). She is a member of the IEI. She holds a MTech in Power Efficient Hierarchical Centralized Routing in WSN from NIT Hamirpur and a PhD degree in computer science from Chitkara University, India.

Shivani Sood is currently serving as an assistant professor in the School of Computer Applications at Lovely Professional University, Phagwara, Punjab, India. Her research interests focus on pattern recognition, machine learning, image processing, and data science. Her teaching interests include Python, Java, C/C++ and advanced data structures. She has filed 2 patents and published 10 articles in international journals and conferences. She holds a master’s degree in data guard configuration and management from Sri Sai College of Engineering College, and a Ph.D. degree in computer science from Chitkara University, Punjab, India.

Abhineet Anand is a distinguished academician and researcher with over 24 years of experience in teaching, research, and academic administration. He currently serves as the Dean of the School of Computer Science and Engineering at Bahra University, Shimla Hills, where he has been instrumental in bridging the gap between academia and industry. His research interests include Cloud Computing, Cloud Security, Decision Trees, Nearest Neighbour Methods, Clustering, Rule Induction, Optical Fiber Switching in Wavelength Multiplexing, and Automata Theory. He has an extensive publication record, with more than 65 Scopus-indexed papers, 14 SCI papers, and 16 papers in International Conferences to his credit. Additionally, he has contributed to 12 International Journals and has edited 8+ books, showcasing his expertise in emerging technologies. He has been a session chair/co-chair and a Technical Program Committee member for over 20 international conferences. He is also a thesis supervisor for Liverpool John Moores University through upGrad Education Pvt. Ltd., reflecting his global academic engagement. As a passionate advocate for innovation and interdisciplinary research, he has co-edited several books, including Bio-Inspired Optimization for Medical Data Mining and the newly published Internet of Medicine for Smart Healthcare.

Jayashree Mohanty is currently serving as an Assistant professor at Chandigarh University, Punjab, India. She has more than 15 years of teaching and industrial experience. She has published numerous research articles in journals and conferences of international repute. She has also acted as the member of reviewer board for leading publishing houses and international conferences. Her research interests include blockchain technology, bigdata engineering, Artificial Intelligence and Machine Learning.