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.
- Includes case studies on the application of AI and machine learning for monitoring climate change effects and management
- Features applications of software and algorithms for modeling and forecasting climate change
- Shows how real-time monitoring of specific factors (temperature, level of greenhouse gases, rain fall patterns, etc.) are responsible for climate change and possible mitigation efforts to achieve environmental sustainability
1. Climate uncertainties and biodiversity: An overview
Rohit Kamboj
2. Historical perspectives on climate change and its negative impacts on the
nature
Shama E. Haque
3. Impact of climate change on water quality and its assessment
Sunita Verma
4. Climate change impacts on water resources and adaptation strategies
Sukanya raghavan
5. Impact of Plastics in the Socio-economic disaster of Climate Change: The
Roadblocks of Sustainability
Arnab Banerjee
6. Impression of Climatic Variation on Flora, Fauna and Human Being: A
present State of Art
Dipankar Ghosh
7. Impact of air quality as a component of climate change on
biodiversity-based ecosystem services
Sylvester Chibueze Izah
8. Role of Climate Change in disasters occurrences: Forecasting and
Management options
Alok Pratap Singh
9. Forecasting and management of disasters triggered by climate change
Fatemeh Rajabi
10. El-Niño Southern Oscillation and its Effects
Sayantika Mukherjee
11. Impact of socio-economic parameters on adoption of climate resilient
technology under varying vulnerability conditions: Evidences from Himalayan
Region
Pardeep Singh
12. Modelling and forecasting of climate change effects using artificial
intelligence techniques
Rajib Maity
13. The role of artificial intelligence strategies to mitigate abiotic stress
and climate change in crop production
Richa Saxena
14. Application of Artificial Intelligence in Environmental Sustainability
and Climate Change
Neeta Kumari
15. Machine learning approaches for climate change impact assessment in
agriculture production
Swati Singh
16. Benchmarking of traditional and advanced machine Learning modelling
techniques for prediction of solarradiation
Dwijendra Nath Dwivedi
17. Concept of climate smart villages using artificial intelligence/machine
learning
Purnima Mehta
18. Significance of AI to develop mitigation strategies against climate
change in accordance with sustainable development goal (climate action)
Vijaya Ilango
19. A cross-sectional study about the impacts of climate change on the flora,
fauna and human society of Odisha, India
Manojit Bhattacharya
20. Development of mitigation strategies for the climate change using
artificial intelligence to attain sustainability
Kartikey Sahil
21. Role of artificial intelligence in environmental sustainability.
Mohamed Habila
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. Dr. Abhishek Kumar is Assistant Director and Associate Professor in the Department of Computer Science and Engineering at Chandigarh University, Punjab, India. He completed his PhD in computer science at the University of Madras (India), and previously worked as a post-doctorate fellow in computer science at Ingenium Research Group, based at the Universidad de Castilla-La Mancha in Spain. He has been teaching in academia for more than 13 years, and has over 160 publications in peer reviewed national and international journals, books, and conferences. His research area includes artificial intelligence, renewable energy applications, image processing, computer vision, data mining, and machine learning.
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. 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. Dr. Arun Lal Srivastav is an Associate Professor in the Department of Applied Sciences at Chitkara University, Himachal Pradesh, India.
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