Computational Modeling Applications for Climate Crisis
- 1st Edition - September 26, 2024
- Editors: Utku Kose, Deepak Gupta, Jose Antonio Marmolejo Saucedo
- Language: English
- Paperback ISBN:9 7 8 - 0 - 4 4 3 - 2 1 9 0 5 - 4
- eBook ISBN:9 7 8 - 0 - 4 4 3 - 2 1 9 0 6 - 1
Computational Modeling Applications for Climate Crisis provides readers with innovative research on the applications of computational modeling to moderate climate change. The bo… Read more
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Request a sales quoteComputational 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.
- Illustrates how computational modeling techniques can be used for dealing with the climate crisis, including simulations, multi-mode-data, usage, and visualization-based research
- Provides case studies demonstrating innovative solutions to moderate climate change, including mathematical, visual, and multi-data-based findings of applied research
- Authored by leading researchers in computational modeling
Researchers in computational modelling, applied mathematicians, and computer scientists. Researchers, scientists, and developers in the fields of Data Science, Statistics, Biomedical Engineering, Artificial Intelligence, and Data Processing, as well as researchers in the fields of Biology, Geology, Physics, Chemistry, Electronics, and Communication Engineering
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- List of contributors
- About the editors
- Foreword
- Preface
- Acknowledgement
- 1. Artificial intelligence and optimized computational modelling against climate crisis
- Abstract
- 1.1 Introduction
- 1.2 Overview of climate crisis
- 1.3 Proposed methodology
- 1.4 Advantages
- 1.5 Disadvantages
- 1.6 Characteristics
- 1.7 Challenges
- 1.8 Case study
- 1.9 Opportunities
- 1.10 Future perspectives
- 1.11 Conclusion
- References
- 2. Computational analysis of recurrent neural network models for precipitation forecasting: a case study in Burdur, Türkiye
- Abstract
- 2.1 Introduction
- 2.2 Methodology
- 2.3 Experiment
- 2.4 Results
- 2.5 Conclusion
- References
- 3. Sustainable development goal-13: a case of zero carbon footprint
- Abstract
- 3.1 Introduction
- 3.2 Discussion and results
- 3.3 Geospatial approach for climate change
- 3.4 Conclusion
- References
- 4. Evaluating computational time series methods for monthly rainfall forecasting in the wettest region of Türkiye: a case study of Rize province
- Abstract
- 4.1 Introduction
- 4.2 Literature review
- 4.3 Overview of study area and data used
- 4.4 Methodology
- 4.5 Results and discussion
- 4.6 Conclusion
- References
- 5. Quantum machine learning for weather forecasting studies
- Abstract
- 5.1 Introduction
- 5.2 Related work
- 5.3 Mathematical modeling
- 5.4 Implementation
- 5.5 Discussion
- 5.6 Conclusion and future scope
- References
- 6. Emerging technologies approach for climate modeling
- Abstract
- 6.1 Introduction
- 6.2 Carbon monoxide pollutant—statistical trends in Visakhapatnam
- 6.3 Forest fires—naturally air pollution—human hazardous
- 6.4 Climate change adaptation
- 6.5 Food security—impact of climate change
- 6.6 Forecasting climate and weather
- 6.7 Physics-guided machine learning—physically informed parameterization
- 6.8 Conclusion
- References
- 7. Computational modeling for agriculture and climate change relation
- Abstract
- 7.1 Introduction
- 7.2 Preliminaries
- 7.3 Climate change, agricultural adaptation strategies, and computational modeling
- 7.4 Conclusion
- References
- 8. The energy from PM2.5 for better climate: a case of sustainable development
- Abstract
- 8.1 Introduction
- 8.2 Contaminants through particulate matter
- 8.3 Nitrogen di oxide pollution—childhood asthma
- 8.4 Carbon monoxide
- 8.5 Climate change—global warming
- 8.6 Generalized linear model—air pollution in India
- 8.7 The need for an air quality index
- 8.8 Waste to energy—recycle of PM2.5
- 8.9 Nanotechnology toward waste to energy
- 8.10 Photoelectrochemical: carbon dioxide
- 8.11 Conclusion
- References
- Chapter 9. Development of deep learning models for climate change within python framework
- Abstract
- 9.1 Introduction
- 9.2 Deep learning models for climate change
- 9.3 Operations in python framework using Jupyter Notebook
- 9.4 Exploring visualizations for climate models
- 9.5 Conclusions and recommendations
- References
- 10. Computational modeling approaches on tourism and climate change relations
- Abstract
- 10.1 Introduction
- 10.2 Connections between tourism and climate change
- 10.3 Computational modeling for tourism and climate change
- 10.4 Conclusion and future work
- References
- 11. Modeling responsible technologies using multiagent system for climate crisis and sustainability
- Abstract
- 11.1 Introduction
- 11.2 Responsible technologies and sustainability
- 11.3 The role of modeling and simulation
- 11.4 Modeling responsible technologies with multiagent system
- 11.5 Proposed multiagent system integrated decentralized energy systems
- 11.6 Discussion
- 11.7 Conclusion
- References
- 12. Modeling the effect of meteorological drought on lake level changes with machine learning techniques
- Abstract
- 12.1 Introduction
- 12.2 Materials and methods
- 12.3 Results and discussion
- 12.4 Conclusions
- References
- Index
- No. of pages: 300
- Language: English
- Edition: 1
- Published: September 26, 2024
- Imprint: Morgan Kaufmann
- Paperback ISBN: 9780443219054
- eBook ISBN: 9780443219061
UK
Utku Kose
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.
Affiliations and expertise
Associate Professor, Department of Computer Engineering, Süleyman Demirel University, Isparta, TurkeyDG
Deepak Gupta
Dr. Aditya Khamparia has expertise in teaching, entrepreneurship, and research and development of 11 years. He is presently working as Assistant Professor in Babasaheb Bhimrao Ambedkar University, Satellite Centre, Amethi, India. He received his Ph.D. degree from Lovely Professional University, Punjab, India in May 2018. He has completed his M. Tech. from VIT University, Vellore, Tamil Nadu, India and B. Tech. from RGPV, Bhopal, Madhya Pradesh, India. He has completed his PDF from UNIFOR, Brazil. He has published around 105 research papers along with book chapters including more than 25 papers in SCI indexed Journals with cumulative impact factor of above 100 to his credit. Additionally, he has authored and edited eleven books. Furthermore, he has served the research field as a Keynote Speaker/Session Chair/Reviewer/TPC member/Guest Editor and many more positions in various conferences and journals. His research interest include machine learning, deep learning for biomedical health informatics, educational technologies, and computer vision.
Affiliations and expertise
Assistant Professor, Department of Computer Science and Engineering, Maharaja Agrasen Institute of Technology, Delhi, IndiaJM
Jose Antonio Marmolejo Saucedo
Dr. Jose Antonio Marmolejo Saucedo is a Professor at Panamerican University, Mexico. His research is on large-scale optimization techniques, computational techniques, analytical methods for planning, operations, and control of electric energy and logistic systems, sustainable supply chain design, and digital twins in supply chains. He received his PhD in Operations Research (Hons) at National Autonomous University of Mexico. He is a member of the Network for Decision Support and Intelligent Optimization of Complex and Large Scale Systems, Mexican Society for Operations Research and System Dynamics Society. He is the author/editor of Computational Intelligence for Covid-19 and Future Pandemics, Springer; Modeling, Simulation, and Optimization, Springer; Data Analysis and Optimization for Engineering and Computing Problems, Springer; Artificial Intelligence for Renewable Energy and Climate Change, Wiley; Intelligent Computing and Optimization, Springer; and Innovative Computing Trends and Applications, Springer; among others.
Affiliations and expertise
Professor Panamerican University, Mexico City, MexicoRead Computational Modeling Applications for Climate Crisis on ScienceDirect