Computational modeling applications for climate crisis / edited by Utku Kose, Deepak Gupta, Jose Antonio Marmolejo Saucedo..

Format:

Book

Contributor:

Kose, Utku, 1985- editor.

Gupta, Deepak, Ph.D., editor.

Marmolejo Saucedo, Jose Antonio, editor.

Series:

Computational Modeling Applications for Existential Risks.

Computational Modeling Applications for Existential Risks Series

Language:

English

Subjects (All):

Computer simulation.

Climatic changes--Mathematical models.

Climatic changes.

Climate change mitigation--Data processing.

Climate change mitigation.

Big data--Environmental aspects.

Big data.

Physical Description:

1 online resource (282 pages)

Edition:

1st ed.

Place of Publication:

Cambridge, MA : Morgan Kaufmann, [2025]

Summary:

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. 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.

Contents:

Front Cover

Computational Modeling Applications for Climate Crisis

Copyright Page

Dedication

Contents

List of contributors

About the editors

Foreword

Preface

Acknowledgement

1 Artificial intelligence and optimized computational modelling against climate crisis

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

2.1 Introduction

2.2 Methodology

2.2.1 Problem statement

2.2.2 Recurrent neural networks

2.2.3 Long short-term memory

2.2.4 Gated recurrent units

2.3 Experiment

2.3.1 Study area and data

2.3.2 Performance metrics

2.4 Results

2.5 Conclusion

3 Sustainable development goal-13: a case of zero carbon footprint

3.1 Introduction

3.2 Discussion and results

3.2.1 Sea level increase

3.2.2 Radiation from cell phones or towers

3.2.3 Emission from information communication and technology industries

3.2.4 Artificial neural network-calculation for emission rate

3.2.5 Pollutant emission and air quality index-Delhi 2020

3.2.6 Renewable energy, clean energy, and hydropower

3.2.6.1 Hydropower

3.2.7 Climate change with extreme rising temperature globally

3.3 Geospatial approach for climate change

3.4 Conclusion

4 Evaluating computational time series methods for monthly rainfall forecasting in the wettest region of Türkiye: a case st...

4.1 Introduction

4.2 Literature review

4.3 Overview of study area and data used

4.4 Methodology

4.4.1 Autoregressive integrated moving average.

4.4.2 Prophet model

4.4.3 Long short-term memory

4.4.4 Performance evaluation criteria

4.5 Results and discussion

4.6 Conclusion

5 Quantum machine learning for weather forecasting studies

5.1 Introduction

5.2 Related work

5.3 Mathematical modeling

5.4 Implementation

5.5 Discussion

5.6 Conclusion and future scope

6 Emerging technologies approach for climate modeling

6.1 Introduction

6.2 Carbon monoxide pollutant-statistical trends in Visakhapatnam

6.3 Forest fires-naturally air pollution-human hazardous

6.3.1 Results of economic loss due to forest fires

6.4 Climate change adaptation

6.5 Food security-impact of climate change

6.6 Forecasting climate and weather

6.6.1 TrajGRU nowcasting

6.6.2 Case study of Italy (E3CI)

6.6.3 Case study of Germany (E3CI)

6.6.4 Probabilistic ocean regime predictions using bayesian neural network

6.7 Physics-guided machine learning-physically informed parameterization

6.8 Conclusion

7 Computational modeling for agriculture and climate change relation

7.1 Introduction

7.2 Preliminaries

7.3 Climate change, agricultural adaptation strategies, and computational modeling

7.3.1 Basic mathematical expressions and models for climate change, agricultural adaptation strategies, and computational m...

7.3.2 The role of computational modeling in analyzing the impacts of climate change on agriculture

7.3.3 Advances in computational models for climate change and agriculture

7.3.4 Computational modeling case studies and applications in agriculture and climate change research

7.3.5 Challenges and limitations of computational modeling for agriculture and climate change

7.3.6 Future perspectives and opportunities

7.4 Conclusion

References.

8 The energy from PM2.5 for better climate: a case of sustainable development

8.1 Introduction

8.2 Contaminants through particulate matter

8.2.1 Particulate matter 2.5 um from various sources-Australia

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

8.6.1 Hourly trends of air pollutants in Visakhapatnam

8.6.2 Air quality index hourly trends: April, July, December 2022, and January 2023-Visakhapatnam

8.6.3 Monthly trends of air pollutants in Visakhapatnam for April, July, and December 2022

8.6.4 Monthly trends of air quality index in Visakhapatnam

8.6.5 Hourly trends of air pollutants in Shastri Nagar

8.6.6 Air quality index hourly trends: April, July, December 2022 and January 2023-Shastri Nagar

8.6.7 Monthly Trends of Air Pollutants in Shastri Nagar in April, July, and December 2022

8.6.8 Monthly trends of air quality index in Shastri Nagar

8.6.9 Hourly trends of air pollutants in Nehru Nagar (Delhi)

8.6.10 Monthly trends of air pollutants in Nehru Nagar (Delhi) in April, July, and December 2022

8.6.11 Air quality index hourly trends: April, July, December 2022, and January 2023-Nehru Nagar

8.6.12 Monthly trends of air quality index in Nehru Nagar

8.7 The need for an air quality index

8.8 Waste to energy-recycle of PM2.5

8.8.1 Waste to value energy conversion: PM2.5 for electrodes

8.9 Nanotechnology toward waste to energy

8.9.1 Nanotechnology in plastic recycle

8.9.2 Nanotechnology: a unique solution for air pollution

8.9.2.1 Catalyst

8.9.2.2 Nanosensors

8.9.2.3 Nanobioremediation

8.10 Photoelectrochemical: carbon dioxide

8.11 Conclusion

9 Development of deep learning models for climate change within python framework

9.1 Introduction.

9.1.1 Global challenge

9.1.2 Artificial intelligence

9.1.3 Machine learning

9.1.4 Deep learning

9.2 Deep learning models for climate change

9.2.1 Recurrent neural networks

9.2.2 Long short-term memory networks

9.2.3 Gated recurrent unit networks

9.2.3.1 Long short-term memory versus gated recurrent unit

9.2.4 Mathematical equations for MSE, MAE, MAPE, and R2

9.2.5 Mathematical equations for optimizers: Adam, SGD, Adamax, Adagrad, Adadelta, RMSProp, and Nadam

9.2.5.1 Adagrad

9.2.5.2 Adamax

9.2.5.3 Adam

9.2.5.4 SGD

9.2.5.5 Adadelta

9.2.5.6 RMSProp

9.2.5.7 Nadam

9.3 Operations in python framework using Jupyter Notebook

9.4 Exploring visualizations for climate models

9.5 Conclusions and recommendations

10 Computational modeling approaches on tourism and climate change relations

10.1 Introduction

10.2 Connections between tourism and climate change

10.2.1 Technology factor

10.2.2 Research on tourism and climate change relations

10.2.2.1 Effects of climate change on tourism

10.2.2.2 Effects of tourism regarding climate change

10.3 Computational modeling for tourism and climate change

10.3.1 Computational models for effects of climate change on tourism

10.3.2 Computational models for effects of tourism on climate

10.3.3 Computational model suggestions

10.4 Conclusion and future work

11 Modeling responsible technologies using multiagent system for climate crisis and sustainability

11.1 Introduction

11.2 Responsible technologies and sustainability

11.2.1 Environmental impact

11.2.2 Climate change mitigation

11.2.3 Resource efficiency

11.2.4 Biodiversity conservation

11.2.5 Social equity

11.2.6 Economic stability

11.2.7 Health and well-being

11.2.8 Education and knowledge transfer.

11.2.9 Community engagement

11.2.10 Ethical and responsible governance

11.3 The role of modeling and simulation

11.4 Modeling responsible technologies with multiagent system

11.4.1 Agent representation

11.4.2 Ecosystem modeling

11.4.3 Climate simulation

11.4.4 Land-use change modeling

11.4.5 Pollution and resource usage modeling

11.4.6 Scenario analysis

11.4.7 Predicting adaptation and resilience

11.5 Proposed multiagent system integrated decentralized energy systems

11.5.1 Agent representation

11.5.2 Optimizing energy distribution

11.5.3 Resilience and reliability

11.5.4 Market mechanisms

11.5.5 Optimization and learning

11.6 Discussion

11.7 Conclusion

12 Modeling the effect of meteorological drought on lake level changes with machine learning techniques

12.1 Introduction

12.2 Materials and methods

12.2.1 Study region

12.2.2 Standardized precipitation index

12.2.3 Machine learnings algorithms

12.2.3.1 k-nearest neighbors

12.2.3.2 Support vector regression

12.2.3.3 Adaptive boosting

12.2.3.4 Random forest

12.2.3.5 Extra tree algorithm

12.2.3.6 Gradient boosting regressor

12.2.3.7 eXtreme gradient boosting

12.2.3.8 Category boosting

12.3 Results and discussion

12.4 Conclusions

Index

Back Cover.

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

9780443219061

0443219060

OCLC:

1458290234