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. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. List of contributors - p. xiii 8. About the editors - p. xv 9. Foreword - p. xvii 10. Preface - p. xix 11. Acknowledgement - p. xxi 12. 1. Artificial intelligence and optimized computational modelling against climate crisis - p. 1 13. 2. Computational analysis of recurrent neural network models for precipitation forecasting: a case study in Burdur, Türkiye - p. 15 14. 3. Sustainable development goal-13: a case of zero carbon footprint - p. 33 15. 4. Evaluating computational time series methods for monthly rainfall forecasting in the wettest region of Türkiye: a case study of Rize province - p. 49 16. 5. Quantum machine learning for weather forecasting studies - p. 73 17. 6. Emerging technologies approach for climate modeling - p. 85 18. 7. Computational modeling for agriculture and climate change relation - p. 107 19. 8. The energy from PM2.5 for better climate: a case of sustainable development - p. 125 20. Chapter 9. Development of deep learning models for climate change within python framework - p. 159 21. 10. Computational modeling approaches on tourism and climate change relations - p. 187 22. 11. Modeling responsible technologies using multiagent system for climate crisis and sustainability - p. 203 23. 12. Modeling the effect of meteorological drought on lake level changes with machine learning techniques - p. 227 24. Index - p. 247
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. Title of Book - p. i 2. Cover image - p. i 3. Title page - p. ii 4. Table of Contents - p. iii 5. Copyright - p. iv 6. Dedication - p. v 7. List of contributors - p. xiii 8. About the editors - p. xv 9. Foreword - p. xvii 10. Preface - p. xix 11. Acknowledgement - p. xxi 12. 1. Artificial intelligence and optimized computational modelling against climate crisis - p. 1 13. 2. Computational analysis of recurrent neural network models for precipitation forecasting: a case study in Burdur, Türkiye - p. 15 14. 3. Sustainable development goal-13: a case of zero carbon footprint - p. 33 15. 4. Evaluating computational time series methods for monthly rainfall forecasting in the wettest region of Türkiye: a case study of Rize province - p. 49 16. 5. Quantum machine learning for weather forecasting studies - p. 73 17. 6. Emerging technologies approach for climate modeling - p. 85 18. 7. Computational modeling for agriculture and climate change relation - p. 107 19. 8. The energy from PM2.5 for better climate: a case of sustainable development - p. 125 20. Chapter 9. Development of deep learning models for climate change within python framework - p. 159 21. 10. Computational modeling approaches on tourism and climate change relations - p. 187 22. 11. Modeling responsible technologies using multiagent system for climate crisis and sustainability - p. 203 23. 12. Modeling the effect of meteorological drought on lake level changes with machine learning techniques - p. 227 24. Index - p. 247
