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Elsevier

Water Sustainability and Predictive Modelling

Insights for Sustainable Water Management

  • 1 Edición - 1 de marzo de 2027
  • Última edición
  • Editores: Manish Kumar, Vivek Agarwal, Vijay P. Singh
  • Idioma: Inglés

Water Sustainability and Predictive Modelling addresses one of the most urgent challenges of our time—managing water resources in an era marked by climate change, urban growth… Leer más

Descripción

Water Sustainability and Predictive Modelling addresses one of the most urgent challenges of our time—managing water resources in an era marked by climate change, urban growth, and increasing demand. This comprehensive volume traces the evolution of water management from traditional practices to innovative, data-driven solutions. By harnessing the power of cutting-edge technologies such as artificial intelligence, machine learning, and advanced hydrological modeling, it showcases how predictive approaches can accurately forecast water availability, identify risks, and support sustainable decision-making. Designed for students, researchers, policymakers, and industry leaders, this book offers practical insights into optimizing water use. Featuring real-world case studies and expert perspectives, it provides actionable strategies to enhance water security, promote resource efficiency, and foster resilient water governance. Whether addressing water scarcity, flood risks, or climate impacts, this volume empowers readers to drive innovation and build a sustainable water future.

Puntos claves

  • Includes comprehensive data visualizations and heatmaps that effectively illustrate climate models, hydrological simulations, and AI-driven forecasts to highlight trends and spatial variations
  • Provides the latest information on predictive modeling and hydrological innovations
  • Features real-world photographs, case studies, and infographics that utilize color to emphasize key project outcomes, ecological insights, and comparative analyses for better understanding and engagement

De interès para

Students and researchers of hydrology

Índice

1. Introduction to Predictive Modelling in Water Sustainability

2. Predictive Modelling for Surface Water and Groundwater Interactions

3. AI and Machine Learning for Water Forecasting

4. Predictive Modelling for Climate Change Impacts on Water Resources

5. Predictive Modelling for Agricultural Water Use and Sustainable Irrigation

6. Hydrological Modelling for Water Availability and Flood Prediction

7. Predictive Modelling for Industrial Water Management and Circular Economy

8. Smart Water Infrastructure and IoT-Based Monitoring

9. Predictive Modelling for Carbon Neutrality in Waste Management

10. Policy, Governance, and Decision-Support Systems for Water Sustainability

11. Multi-Scale Predictive Modelling for Urban Water Security

12. Future Directions in Water Sustainability and Predictive Modelling

Detalles del producto

  • Edición: 1
  • Última edición
  • Publicado: 1 de marzo de 2027
  • Idioma: Inglés

Sobre los editores

MK

Manish Kumar

Dr. Manish Kumar, Associate Professor, Amity University, Noida, Uttar Pradesh, India has done his PhD in Microbiology from ICAR-National Bureau of Agriculturally Important Microorganisms & RD University, Jabalpur. He has expertise in the field of plant-microbe interaction, microbial metagenomics, microbial diversity, and in-silico vaccine designing. He has qualified ICAR-NET with experiences like RA and SRF in ICAR and CSIR labs. He is a recipient of SERB SIRE (SERB International Research Experience-2022) fellowship from DST (Department of Science & Technology), Govt. of India. He worked under the guidance of Dr. Marina Kalyuzhnaya, Professor, Department of Biology, San Diego State University, USA. He is the Editor and reviewer of different reputed research journals in the field of microbiology. He has organized several workshops, and national/international conferences as organizing and joint organizing secretary.

Afiliaciones y experiencia
Associate Professor, Amity University, Noida, Uttar Pradesh, India

VA

Vivek Agarwal

Vivek Agarwal is a Lecturer at the School of Engineering, Northumbria University, Newcastle, United Kingdom. Before this, he pursued his PhD from the University of Nottingham in Geospatial Environment Engineering. His thesis title was “Study of groundwater properties and behavior using geospatial tools”, and he was awarded “the best PhD Thesis award” for his doctoral thesis. Dr. Agarwal has published 10+ international publications. He has a wide-ranging research interest in subjects related to civil engineering and interdisciplinary areas. His expertise is in remote sensing, GIS, water supply, hydraulics, fluid mechanics, water quality and sewage pollution. He intends to combine NASA’s GRACE gravity anomalies relating and InSAR land subsidence data relating them to groundwater quantity and quality variations. The aim of the research is to find solutions to the Climate Emergency by better understanding and managing the earth’s groundwater resources.
Afiliaciones y experiencia
Lecturer, School of Engineering, Northumbria University, Newcastle, UK

VS

Vijay P. Singh

Professor. Vijay P. Singh is a globally renowned hydrologist and engineer, currently a Distinguished Professor and Regents Professor at Texas A&M University, where he holds the Caroline and William N. Lehrer Distinguished Chair in Water Engineering. With a career spanning decades, his contributions to surface and groundwater hydrology, hydraulics, irrigation, and water resources management are widely recognized. He has authored over 1,200 journal articles, numerous textbooks, and handbooks, significantly shaping the field. His research encompasses watershed modeling, climate change impacts, and entropy-based hydrologic theories. Prof. Singh's outstanding contributions have earned him prestigious accolades, including the Ven Te Chow Award, Ray K. Linsley Award, and the Norman Medal. Beyond research, he serves as Editor-in-Chief for key hydrology journals, further advancing the discipline.
Afiliaciones y experiencia
Texas A&M University, College Station, TX, USA