Editors:Supreeti Kamilya, Arindam Biswas, Sheng-Lung Peng
Paperback ISBN:978-981-97-1520-6
eBook ISBN:978-981-97-1518-3
This book provides a clear view of various applications for water resource management using different state-of-the-art technologies such as artificial intelligence, IoT, and cellular automata. The book also shows the analytical part of surface water as well as groundwater bodies to control pollution and save ecology. It gives an idea about the collection of data for disaster management such as flood prediction and flood inoculation. The book provides the fundamental aspects of various computational or simulation methods for surface and underground water body detection, prediction of non-biodegradable elements in water bodies, water potability, and predictions of natural disasters like floods. The book summarizes different aspects of water body challenges and the possible solutions proposed using new technologies. The book opens up a future research direction of dealing with various challenges and solutions based on emerging technologies. This book comes up with a direction for the researchers interested in dealing with various aspects of water challenges and finding solutions using emerging technologies in the new era of modern computations.
Water Informatics
Emerging IT
Hydrology
Interdisciplinary Science
Disaster Management
Surface Water Body
Ground Water Body
Pollution Control
State-of-art Technology
Department of Computer Science and Engineering, Birla Institute Of Technology Mesra, Ranchi, India
Supreeti Kamilya
Department of Mining Engineering, Kazi Nazrul University, Asansol, India
Arindam Biswas
Department of Creative Technologies and Product Design, National Taipei University of Business, New Taipei, Taiwan
Sheng-Lung Peng
Book Title
Water Informatics
Book Subtitle
Challenges and Solutions Using State of Art Technologies
Editors
Supreeti Kamilya, Arindam Biswas, Sheng-Lung Peng
Series Title
Water Informatics for Water Resource Management
DOI
https://doi.org/10.1007/978-981-97-1518-3
Hardcover ISBN
978-981-97-1517-6
Published: 23 June 2024
Softcover ISBN
978-981-97-1520-6
Published: 24 June 2025
eBook ISBN
978-981-97-1518-3
Published: 22 June 2024
Series ISSN
2731-9105
Series E-ISSN
2731-9113
Edition Number
1
Number of Pages
XI, 235
Number of Illustrations
19 b/w illustrations, 72 illustrations in colour
Topics
Machine Learning, Computer Applications, Environment, general
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