Computational Modeling Applications for Climate Crisis Computational Modeling Applications for Existential Risks Series

Computational Modeling Applications for Climate Crisis provides readers with innovative research on the applications of computational modeling to moderate climate change. The book begins with an overview and history of climate change, followed by several chapters covering the concepts of computational modeling and simulation, including parameters of climate change, modeling the effects of human activities, visualization tools, and data fusion for advanced modeling applications. It then proceeds to cover decision support systems, modeling of technological solutions for climate change, modeling of greenhouse gas emissions, tracking of climate factors, and modeling of earth resources. In the final chapters of the book, the authors cover nation-based outcomes, big data, and optimization solutions with real-world data and case studies. Climate change is one of the most pressing existential issues for humans and the planet, and this book covers leading-edge applications of computational modeling to the vast array of interdisciplinary factors and challenges posed by climate change. As life itself is a mixture of occurrences that can be mathematically modelled, it is important to work with specific parameters, which are critical for monitoring and controlling the dynamics of the earth, natural resources, technological factors, and human activities.

1. Overview of Climate Change and Crisis
2. Computational Models for Tracking Parameters of the Climate Factors
3. Computational Modeling of Human Activities
4. Simulations for Climate Change
5. Visualizations for Better Interpretability of Climate Parameters
6. Data Fusion for Advanced Numerical Applications Against Climate Crisis
8. Decision Support Applications for Climate Crisis
9. Modeling of Technological Solutions for Climate Change
10. Modeling of Greenhouse Emissions for Predictive Purposes
11. Tracking of Climate Factors for Sustainability
12. Modeling of Earth Resources for Predictive Climate Change Management
13. Nation-based Outcomes for Numerical Analysis of Climate Change
14. Big Data and Computational Modeling Against Climate Crisis
15. Artificial Intelligence and Computational Modeling Against Climate Crisis
16. Modeling for Responsible Use of Technological Components
17. Optimization-Based Solutions to Deal with Climate Change

Dr. Utku Kose is an Associate Professor at Süleyman Demirel University, Turkey. He received his PhD from Selcuk University, Turkey, in the field of computer engineering. He has more than 100 publications to his credit, including Deep Learning for Medical Decision Support Systems, Springer; Artificial Intelligence Applications in Distance Education, IGI Global; Smart Applications with Advanced Machine Learning and Human-Centered Problem Design, Springer; Artificial Intelligence for Data-Driven Medical Diagnosis, DeGruyter; Computational Intelligence in Software Modeling, DeGruyter; Data Science for Covid-19, Volumes 1 and 2, Elsevier/Academic Press; and Deep Learning for Medical Applications with Unique Data, Elsevier/Academic Press, among others. Dr. Kose is a Series Editor of the Biomedical and Robotics Healthcare series from Taylor & Francis/CRC Press. His research interests include artificial intelligence, machine ethics, artificial intelligence safety, optimization, chaos theory, distance education, e-learning, computer education, and computer science.
Dr. Deepak Gupta is an assistant professor at Maharaja Agrasen Institute of Technology, Delhi, India. He is an eminent academician, including roles as lecturer, researcher, consultant, community service, PhD, and post-doctorate supervision. Dr. Gupta focuses on rational and practical learning and has contributed important literature in the fields of Human-Computer Interaction, Intelligent Data Analysis, Nature-Inspired Computing, Machine Learning, and Soft Computing. Dr. Gupta has authored/edited a number of books, including Emerging Trends and Roles of Fog, Edge, and Pervasive Computing in Intelligent IoT-Driven Applications, Wiley; Advanced Machine Intelligence and Signal Processing, Springer; Deep Learning for Medical Applications with Unique Data, Elsevier/Academic Press; Explainable Edge AI: A Futuristic Computing Perspective, Springer; Applications of Big Data in Healthcare, Elsevier/Academic Press; and