Sughosh Madhav

Arun Lal Srivastav

Abhishek Kumar Bhardwaj

Eugenia Valsami-Jones

Urban Water Crisis and Management: Strategies for Sustainable Development, Sixth Edition presents solutions for the current challenges of urban water and management strategies. Through contributed chapters, a framework is laid out for a reduction of the use of groundwater (heavily overused as a solution) and the alternative options for the supply of water to cities, or for urban water. Sections discuss urban water, its problems and management approaches, address the root causes of the water crisis in urban areas, and cover the scientific and technical knowledge necessary to manage water resources. Significant gaps between developed and developing nations in the procedure of water management are also addressed, along with practical information regarding recycling and the reuse of wastewater which is useful as baseline data for the future. - Presents the quantitative study of water supply in urban areas, identifies water scarcity in megacities, and provides management approaches for sustainable development - Identifies technology and the instruments required for the management and safe supply of water - Includes case studies where these technologies have been successfully used

Strategies for Sustainable Development

Urban Water Crisis and Management

Abhishek Kumar

Ashutosh Kumar Dubey

Fausto Pedro Garcia Marquez

Vicente García-Díaz

Umesh Chandra Pati

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 systems - Builds knowledge and tools in a systematic manner, from fundamentals to advanced applications - Includes dedicated chapters with case studies and applications for each sustainable energy source

Computer Vision and Machine Intelligence for Renewable Energy Systems

Sushil Kumar Narang

Moonis Ali Khan

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

Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence

Arun Lal Srivastav

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