Computational Intelligence for Climate Change by Anand, A. Jose; Krishnan, Saravanan; Leal Filho, Walter;

Long description:

This book stands out in the field due to its interdisciplinary approach, integrating cutting-edge computational methods with environmental sciences to offer practical solutions for sustainable development. One of the most important worldwide issues of our day is climate change, which calls for creative, data-driven, and scalable solutions. A thorough examination of how computational intelligence (CI) methods can be applied to different facets of climate change, from assessing its effects to developing mitigation and adaptation plans, can be found in the book. The chapters explain how these tools can be harnessed to tackle the complexities of climate change. The book contextualizes climate change as a data-rich problem, highlighting the challenges posed by its vast, heterogeneous, and dynamic datasets. The book also discusses about climate data analytics, which discusses the role of CI in managing and analyzing large-scale climate data collected from satellites, weather stations, and IoT devices. This book presents techniques for preprocessing, cleaning, and normalizing climate data to enhance its usability. This book details the use of computational intelligence for creating accurate climate models. This book explores techniques for predicting long-term trends, such as global warming and sea level rise, and short-term events like hurricanes and heatwaves. Case studies demonstrate how machine learning algorithms, such as random forests and support vector machines, have been used to refine predictive models. It presents vulnerability analysis frameworks that use CI to assess the susceptibility of regions, ecosystems, and communities to climate risks. The book discusses real-world applications, such as identifying drought-prone areas using neural networks or assessing flood risks using fuzzy logic systems. The book delves into the integration of CI with emerging technologies such as blockchain, quantum computing, and the Internet of Things (IoT) for more robust climate solutions.

Table of Contents:

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Foundations of Computational Intelligence for Climate Applications.- Big Data Analytics in Climate Science.- Machine Learning Models for Climate Prediction.- Deep Learning Techniques in Climate Forecasting.- Climate Change Impact Analysis Using Fuzzy Systems.- Evolutionary Algorithms for Optimizing Renewable Energy Systems.- Climate Vulnerability Assessment with Neural Networks.- Intelligent Systems for Carbon Emission Monitoring.- CI Applications in Climate-Resilient Agriculture.- Smart Urban Planning: A CI Perspective.- Blockchain-Enabled Transparency in Climate Solutions.- IoT-Driven Data Collection and CI Integration.- Real-Time Weather Anomaly Detection Using CI.- Hybrid Models for Climate Adaptation Strategies.- Geoengineering and CI: Innovations for Climate Mitigation.- Ethical Challenges in Computational Climate Intelligence.- Quantum Computing for Advanced Climate Modeling.- Interdisciplinary Approaches to Climate Intelligence.- Case Studies: Successful CI Applications in Climate Action.- The Future of Computational Intelligence in Combating Climate Change.