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Full Description
This book discusses the adverse effects of climatic changes on our planet. It examines AI-based tools and technologies and how they can assist in identifying energy emission reductions, CO2 removal, and support the development of greener transportation networks, monitoring deforestation, and forecasting extreme weather events.
AI for Climate Change and Environmental Sustainability identifies and discusses in detail the importance of environmental sustainability based on accomplishment of the UN's 17 Sustainable Developmental Goals (SDGs). It presents the various AI-based possibilities for accelerating international efforts to safeguard the environment and conserve natural resources. The authors offer a comprehensive analysis of the emerging field of climate change in relation to Internet of Things, artificial intelligence, machine learning, and deep learning. The book discusses AI developments, applications, and best practices that will help us transition to a low-carbon future on both a regional and global scale. It provides case studies with analytical results pertinent to climate change and weather prediction and includes chapters with a research-oriented approach, which can encourage new developments in the field of sustainable climate and green environment.
The book can be used as a primary textbook for graduate and postgraduate students in technology and science, as well as a reference for researchers, academics, and IT professionals working on climate change and sustainability initiatives.
Contents
Chapter 1- AI For Sustainable and Eco-Friendly Environment For Climate Change
Chapter 2- Enhancing Climate Change Prediction and Risk Assessment with Deep Learning: Architectural Approaches and Data Challenges
Chapter 3- AI- and IoT-Based Application for Rainfall Prediction: A Study
Chapter 4- Machine-Learning Based Prediction of Wind Speed for Ratnagiri Region, India
Chapter 5- Wind Power Forecasting with Machine Learning Approach
Chapter 6- IOT Communication Technologies
Chapter 7- Machine Learning Models for Intelligent Hazard Management
Chapter 8- Optimal Dispatch of Distributed Renewable Energy Sources in Isolated Microgrid System Exploiting Metaheuristic Optimization Algorithms
Chapter 9- Leveraging Machine Learning Models for Intelligent Hazard Management
Chapter 10- Practical and Innovative Applications of IOT and IOT Networks (Smart Cities, Smart Mobility, Smart Home, Smart Health, Smart Grid, etc.)
Chapter 11- Leveraging Artificial Intelligence in Climate Change Interpretation: Overcoming Challenges in Risk Management Approach
