Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence

1st Edition - November 11, 2022
Imprint: Elsevier
Editors: Ashutosh Kumar Dubey, Abhishek Kumar, Sushil Kumar Narang, Moonis Ali Khan, Arun Lal Srivastav
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 9 9 7 1 4 - 0
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 9 9 7 1 5 - 7

Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence covers computer-aided artificial intelligence and machine learning technolog… Read more

Purchase options

World Book Day Celebration!

Save up to 30% on books and eBooks!

Shop now

Institutional subscription on ScienceDirect

Request a sales quote

Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence covers computer-aided artificial intelligence and machine learning technologies as related to the impacts of climate change and its potential to prevent/remediate the effects. As such, different types of algorithms, mathematical relations and software models may help us to understand our current reality, predict future weather events and create new products and services to minimize human impact, chances of improving and saving lives and creating a healthier world.

This book covers different types of tools for the prediction of climate change and alternative systems which can reduce the levels of threats observed by climate change scientists. Moreover, the book will help to achieve at least one of 17 sustainable development goals i.e., climate action.

Cover Image
Title Page
Copyright
Table of Contents
Contributors
Chapter 1 Climate uncertainties and biodiversity: An overview
1.1 Introduction
1.2 Effect of climate change on fauna including Homo sapiens
1.3 Effect of climate change on health of humans
1.4 Whether we are adjusting to change in climate
1.5 Applications of artificial intelligence and machine learning
1.6 Conclusion
References
Chapter 2 Historical perspectives on climate change and its influence on nature
2.1 Introduction
2.2 Ancient cultures and climate change
2.3 Global warming and climate change
2.4 Public perception and knowledge of climate change
2.5 Intergovernmental panel on climate change
2.6 Emergence of climate change legislation
2.7 Climate change and environmental activism
2.8 The 2021 United Nations climate change conference
2.9 Climate change mitigation and adaptation challenges
2.10 Conclusions
References
Chapter 3 Impact of climate change on water quality and its assessment
3.1 Introduction
3.2 Climate change: A global concern
3.3 Climate change and impact on water
3.4 Impact of climate change on groundwater
3.5 Observed and projected change under the influence of climate change
3.6 Summary
References
Chapter 4 Climate change impacts on water resources: An overview
4.1 Introduction
4.2 Observed climate change impacts
4.3 Modeling approaches
4.4 Sustainable water resources management using AI/ML under changing climate
4.5 Hybrid models
4.6 Conclusions and outlook
References
Chapter 5 Impact of plastics in the socio-economic disaster of pollution and climate change: The roadblocks of sustainability in India
5.1 Introduction
5.2 Plastic and the environment: A brief overview
5.3 The role of plastic in climate change
5.4 The social impacts of plastics, its pollution, and climate change: A necessary evil
5.5 The plastic industry and the economy
5.6 Implications of a plastic ban
5.7 Law and the plastic fiasco: India in the global context
5.8 Conclusion
Conflicts of interest
References
Chapter 6 Impression of climatic variation on flora, fauna, and human being: A present state of art
6.1 Introduction
6.2 Climatic warming and their influences on biodiversity
6.3 Environmental stress factors on agriculture productivity
6.4 Effects of climate changes on food web
6.5 Encroachment of climatic changes upon genetics-based diversity and evolutionary biology of fauna
6.6 Climatic alteration effects on genetic diversity and evolutionary biology of human
6.7 Metabolic engineering and synthetic biology approaches toward minimize various climate change issues for improving environmental conditions
6.8 Conclusions
Acknowledgment
References
Chapter 7 Impact of air quality as a component of climate change on biodiversity-based ecosystem services
7.1 Introduction
7.2 Air pollution and climate change
7.3 Effects of climate change and air quality on human population and infrastructure
7.4 Impact of climate change and air quality on human health
7.5 Impacts of climate change and air quality on biodiversity
7.6 Mitigative and adaptive strategies toward sustainability
7.7 Discussions of the interplay of factors promoting climate change and biodiversity loss
7.8 Conclusion
References
Chapter 8 Role of climate change in disasters occurrences: Forecasting and management options
8.1 Introduction
8.2 Climate change and its effects
8.3 Forecasting of climate changes
8.4 Disasters due to temperature and rainfall
8.5 Disasters due to urbanization
8.6 Management in terms of wetland utilization and agricultural discharge
8.7 Future perspectives
8.8 Conclusion
References
Chapter 9 Forecasting and management of disasters triggered by climate change
9.1 Introduction
9.2 Disaster risk management cycle
9.3 Forecasting and disaster management
9.4 Summary and conclusion
References
Chapter 10 El-Niño Southern Oscillation and its effects
10.1 Introduction
10.2 Impact on global weather and climate
10.3 Indirect impact of ENSO
10.4 The profit or forfeiture outcome of EL NINO
10.5 Conclusion
10.6 The benefits of socialism
References
Chapter 11 Impact of socioeconomic parameters on adoption of climate resilient technology under varying vulnerability conditions: Evidences from Himalayan region
11.1 Introduction
11.2 Climate change resilience is necessary for the sustainable growth
11.3 About Himachal Pradesh
11.4 Strategies adopted to mitigate climate change impacts
11.5 Change in timing of sowing and harvesting of crops
11.6 Change in crop length period of different crops
11.7 Change in fertilizer, farm yard manure, pesticide, insecticide, and weedicide use
11.8 Strategies adopted in order to cope with the climate change for different crops
11.9 Factors influencing the choice of the strategy
11.10 Conclusions and policy implications
References
Chapter 12 Artificial intelligence/machine learning techniques in hydroclimatology: A demonstration of deep learning for future assessment of stream flow under climate change
12.1 Introduction
12.2 Artificial intelligence techniques in hydroclimatological/hydrometeorological problems
12.3 Application of deep learning techniques in simulating and forecasting streamflow
12.4 Concluding remarks and way forward
References
Chapter 13 The role of artificial intelligence strategies to mitigate abiotic stress and climate change in crop production
13.1 Introduction
13.2 Accelerating climate changes in plant breeding by applying artificial intelligence
13.3 Effect of abiotic stress on crops
13.4 Physiological changes
13.5 Biochemical and molecular changes
13.6 Artificial intelligence as a tool to improve the resilience of crop production
13.7 Databases of artificial intelligence involved in crop production
13.8 Drones-dependent agricultural practices: agricultural drones
13.9 Application of drones in the agriculture sector
13.10 Application of big data and Internet of Things in agriculture
13.11 Robotics in farm management
13.12 Applications of robotics in agriculture
13.13 Rainfall prediction
13.14 Evaluation of crop evapotranspiration
13.15 Estimation of air precipitation
13.16 Estimation of dew point temperature
13.17 Conclusion and future prospects of artificial intelligence in crop management
References
Chapter 14 Application of artificial intelligence in environmental sustainability and climate change
14.1 Introduction
14.2 Artificial intelligence
14.3 SDGs and AI
14.4 Application of AI in environment sustainability
14.5 Challenges of AI
14.6 Conclusion
References
Chapter 15 Machine learning approach for climate change impact assessment in agricultural production
15.1 Introduction
15.2 Crop yield and climate change
15.3 Crop response or adaptation to increased climatic stress
15.4 Modeling approaches to monitor climate change impacts
15.5 Application of remote sensing (RS) and geographic information system (GIS)
15.6 Machine learning techniques
15.7 Application of various machine learning techniques in agriculture
15.8 Conclusion
Conflict of Interest
References
Chapter 16 Climate change: Prediction of solar radiation using advanced machine learning techniques
16.1 Introduction
16.2 Literature review
16.3 Approach
16.4 Results and discussions
16.5 Conclusion
16.6 Discussions: climate change and solar radiation prediction
References
Chapter 17 Concept of climate smart villages using artificial intelligence/machine learning
17.1 Introduction
17.2 The CSV procedure being segmented into several steps
17.3 Climate smart villages approach divided into different steps for decision support to farmers
17.4 Climate change resilience for the sustainable development of villages
17.5 Current projects of climate-smart village around the world
17.6 CSV approach in South Asia
17.7 Application of artificial intelligence and machine learning in the development of resilience in agriculture
17.8 Artificial neural networks (ANN) in agriculture
17.9 Climate-smart village with the use of mobile apps to provide crop-specific and weather services to farmers
17.10 Drone technologies adaptation for sustainable agriculture
17.11 Agromet advisory services in India for climate smart agriculture
17.12 GKMS present and future work
17.13 Conclusion
References
Chapter 18 Significance of artificial intelligence to develop mitigation strategies against climate change in accordance with sustainable development goal (climate action)
18.1 Introduction
18.2 Factors affecting the climate change
18.3 Consequences of climate change
18.4 Mitigating measures for adapting to climate change
18.5 Advantages of using artificial intelligence to develop mitigation strategies against climate change
18.6 Artificial intelligence-centered approach for climate change mitigation
18.7 Conclusions
References
Chapter 19 A cross-sectional study about the impacts of climate change on living organisms: A case study of Odisha province of India
19.1 Introduction
19.2 What are the problems and who is responsible for climate change?
19.3 Impact of climate change in Odisha
19.4 Climate change and its impact on Fauna
19.5 Climate change and its impact on flora
19.6 Climate change and its impact on human societies
19.7 Conclusion
References
Chapter 20 Development of mitigation strategies for the climate change using artificial intelligence to attain sustainability
20.1 Introduction
20.2 The artificial intelligence and sustainable development goals
20.3 The application of AI
20.4 Artificial intelligence and its applications to cope with climate change
20.5 AI and remote sensing
20.6 Role of artificial intelligence in environmental management
20.7 Mitigation and adaptation strategies through use of artificial intelligence
20.8 Conclusion and future perspectives
References
Chapter 21 Role of artificial intelligence in environmental sustainability
21.1 Introduction
21.2 The impact of climate change on the environmental resources
21.3 Sustainability-based development
21.4 Application of artificial intelligence (AI) for achieving sustainability
21.5 Artificial intelligence challenges
21.6 Conclusions and recommendations
References
Index

Ashutosh Kumar Dubey

Ashutosh Kumar Dubey is an Associate Professor in the Department of Computer Science and Engineering at Chitkara University, Himachal Pradesh, India. He is also a Postdoctoral Fellow of the Ingenium Research Group Lab, Universidad de Castilla-La Mancha, Ciudad Real, Spain.

Affiliations and expertise

Department of Computer Science and Engineering, Institute of Engineering and Technology, Chitkara University, India

Abhishek Kumar

Dr. Abhishek Kumar is a professor and post-doctorate fellow in computer science at Ingenium Research Group, based at Universidad De Castilla-La Mancha in Spain. He has been teaching in academia for more than 8 years, and published more than 50 articles in reputed, peer reviewed national and international journals, books, and conferences. His research area includes artificial intelligence, image processing, computer vision, data mining, and machine learning.

Affiliations and expertise

Professor, Post-doctorate Fellow, Ingenium Research Group, Universidad De Castilla-La Mancha, Spain

Sushil Kumar Narang

Sushil Kumar Narang is Dean and an Associate Professor in the Department of Computer Science & Engineering at Chitkara University, Rajpura, Punjab (India) since 2019. From 2006-2019, He was head of IT department at SAS Institute of IT & Research, Mohali, Punjab (India). From 1996-2006, He was Assistant Professor at Department of Computer Science & Applications, MLN College, Yamuna agar, Haryana (India).He has completed his Ph.D. at Panjab University, Chandigarh (India). His Research on “Feature Extraction and Neural Network Classifiers for Optical Character Recognition for Good quality hand written GurmukhiandDevnagariCharacters” focused on various image processing, machine as well as deep learning algorithms. His research interests lie in the area of programming languages, ranging from theory to design to implementation, Image Processing, Data Analytics and Machine Learning. He has collaborated actively with researchers in several other disciplines of computer science; particularly Machine Learning on real world use cases.He is a certified Deep Learning Engineer from Edureka. He possesses expertise in Object-Oriented Analysis & Design and Development using Java and Python programming using OpenCV in Image Processing and Neural Network construction. He has strong knowledge of C++ and Java with experience in component architecture of product interface. With Solid training and management skills, He has demonstrated proficiency in leading and mentoring individuals to maximize levels of productivity, while forming cohesive team environments.

Affiliations and expertise

Department of Computer Science, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

Moonis Ali Khan

Moonis Ali Khan received his doctoral degree (Ph.D.) in Applied Chemistry from Aligarh Muslim University, Aligarh, India, in 2009. From 2009 to 2011, he worked as a Post-Doctoral Researcher at Yonsei University, South Korea and Universiti Putra Malaysia, Malaysia. In 2011, he joined the Chemistry Department at the King Saud University (KSU), Saudi Arabia as an Assistant Professor. Currently, he is working as an Associate Professor at KSU. He is an interfacial chemist and his research is focused on the synthesis and development of novel materials for environmental remediation applications. To date, he has guided two doctoral students for their respective degrees. He has published more than hundred (research and review) articles and has two U.S. patents to his credit.

Affiliations and expertise

Associate Professor, College of Science, King Saud University, Riyadh, Saudi Arabia

Arun Lal Srivastav

Dr. Arun Lal Srivastav is an Associate Professor in the Department of Applied Sciences at Chitkara University, Himachal Pradesh, India.

Affiliations and expertise

Chitkara University, Himachal Pradesh, Solan, India

Life Sciences

Physical Sciences & Engineering

Social Sciences & Humanities

Health

Visualization Techniques for Climate Change with Machine Learning and Artificial Intelligence

Purchase options

World Book Day Celebration!

Institutional subscription on ScienceDirect

Ashutosh Kumar Dubey

Abhishek Kumar

Sushil Kumar Narang

Moonis Ali Khan

Arun Lal Srivastav

Related books