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001472157 001__ 1472157
001472157 003__ OCoLC
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001472157 019__ $$a1391432672$$a1394927981
001472157 020__ $$a9783031352799
001472157 020__ $$a3031352793
001472157 020__ $$z3031352785
001472157 020__ $$z9783031352782
001472157 0247_ $$a10.1007/978-3-031-35279-9$$2doi
001472157 035__ $$aSP(OCoLC)1391441584
001472157 040__ $$aEBLCP$$beng$$cEBLCP$$dYDX$$dGW5XE$$dEBLCP$$dOCLCQ$$dSFB
001472157 049__ $$aISEA
001472157 050_4 $$aTD353
001472157 08204 $$a363.6/10285$$223/eng/20230801
001472157 24500 $$aEmerging Technologies for Water Supply, Conservation and Management /$$cEtikala Balaji, Golla Veeraswamy, Prasad Mannala, Sughosh Madhav, editors.
001472157 260__ $$aCham :$$bSpringer International Publishing AG,$$c2023.
001472157 300__ $$a1 online resource (xiv, 384 pages) :$$billustrations (chiefly color).
001472157 336__ $$atext$$btxt$$2rdacontent
001472157 337__ $$acomputer$$bc$$2rdamedia
001472157 338__ $$aonline resource$$bcr$$2rdacarrier
001472157 4901_ $$aSpringer Water
001472157 5050_ $$aIntro -- Preface -- Contents -- Contributors -- 1 Assessment of Water Consumers Literacy -- 1.1 Introduction -- 1.2 State of Art -- 1.3 The Quality of Water for Human Consumption -- 1.4 Literacy -- 1.4.1 Literacy in Water Consumption -- 1.5 Artificial Neural Networks-Based Approach -- 1.6 Materials and Methods -- 1.6.1 Research Design -- 1.6.2 Data Collection -- 1.6.3 Location of Study -- 1.6.4 Participants -- 1.6.5 Qualitative Data Processing -- 1.6.6 Artificial Neural Networks -- 1.6.7 Ethical Aspects -- 1.7 Results and Discussion -- 1.7.1 Frequency of Response Analysis
001472157 5058_ $$a1.7.2 Water Consumers' Literacy Assessment -- 1.8 Conclusions -- Annex A -- References -- 2 Machine Learning Applications in Sustainable Water Resource Management: A Systematic Review -- 2.1 Introduction -- 2.2 Conceptualization of Water Resource -- 2.2.1 Water Resource Management (WRM) -- 2.2.2 Tools and Techniques of WRM -- 2.3 Methodology Adopted for Conducting Systematic Review -- 2.4 Approaches and Modeling for Sustainable Water Resource via Machine Learning -- 2.4.1 Maintenance Planning for Water Infrastructure -- 2.4.2 Forecasting Water Demand and Use
001472157 5058_ $$a2.4.3 Observing Dams and Water Reservoirs -- 2.4.4 Water Quality Monitoring and Reporting -- 2.4.5 Use of AI in Assessing Water Quality Using Remote Sensing -- 2.5 Challenges in WRM -- 2.6 Review Findings and Research Gap -- 2.7 Conclusion -- 2.8 Recommendations -- References -- 3 Remote Sensing and Machine Learning Applications for the Assessment of Urban Water Stress: A Review -- 3.1 Introduction -- 3.2 Key Indices and Indicators for the Water Quantity and Water Quality -- 3.3 Application of Remote Sensing and Machine Learning for Urban Water Quantity
001472157 5058_ $$a3.4 Application of Remote Sensing and Machine Learning with the Water Quality -- 3.5 Conclusion -- References -- 4 Role of Artificial Intelligence in Water Conservation with Special Reference to India -- 4.1 Introduction -- 4.2 Review Studies in the Modelling of Water Variables -- 4.3 Water Conservation -- 4.4 Managing Water in Megacities -- 4.5 Government of India Water Resource Management Policies and Programs -- 4.5.1 Components -- 4.5.2 National Water Policy -- 4.5.3 Jal Shakti Abhiyan -- 4.5.4 Jal Jeevan Mission -- 4.5.5 Swajal Scheme
001472157 5058_ $$a4.5.6 The National Rural Drinking Water Programme (NRDWP) -- 4.5.7 Nal Se Jal Scheme -- 4.6 Information and Communication Technology Tools in Water Sector -- 4.6.1 Meters and Sensors -- 4.6.2 Sensor for Pressure Management -- 4.6.3 The Flow Sensors -- 4.6.4 Sensors for Energy Consumption -- 4.6.5 Water Consumption Meter -- 4.6.6 Supervisory Control and Data Acquisition (SCADA) -- 4.7 Communication Facilities -- 4.8 Mechanics Models -- 4.9 Decision-Making Aid -- 4.10 Design and Management of Water Supply and Irrigation -- 4.11 Advantages of AI in Water Conservation -- 4.12 Conclusion -- References
001472157 506__ $$aAccess limited to authorized users.
001472157 520__ $$aThis book deals with the role of emerging technologies such as remote sensing and GIS and artificial intelligence/machine learning in water supply, conservation and management for sustainable development. These are low-cost new technologies that address current challenges dealing with large data sets, such as identifying spatial and temporal variations in water quality parameters and contaminants, groundwater potential zones and water supply and management issues. This book is helpful to show the paths of reducing the burden of time and cost and is the alternative options for the conventional practices in water supply, conservation and management. Further, the outcomings of this book are helpful for policy makers, researchers and readers.
001472157 588__ $$aDescription based upon print version of record.
001472157 650_0 $$aWater-supply$$xTechnological innovations.
001472157 650_0 $$aWater-supply$$xManagement.
001472157 650_0 $$aWater conservation$$xTechnological innovations.
001472157 650_0 $$aWater conservation$$xManagement.
001472157 655_0 $$aElectronic books.
001472157 7001_ $$aBalaji, Etikala.
001472157 7001_ $$aVeeraswamy, Golla.
001472157 7001_ $$aMannala, Prasad.
001472157 7001_ $$aMadhav, Sughosh.
001472157 77608 $$iPrint version:$$aBalaji, Etikala$$tEmerging Technologies for Water Supply, Conservation and Management$$dCham : Springer International Publishing AG,c2023$$z9783031352782
001472157 830_0 $$aSpringer water.
001472157 852__ $$bebk
001472157 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-35279-9$$zOnline Access$$91397441.1
001472157 909CO $$ooai:library.usi.edu:1472157$$pGLOBAL_SET
001472157 980__ $$aBIB
001472157 980__ $$aEBOOK
001472157 982__ $$aEbook
001472157 983__ $$aOnline
001472157 994__ $$a92$$bISE