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Jibin Jacob Mani, A.C. Subhajini,
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- Research Scholar,, Associate Professor, Noorul Islam Centre for Higher Education (NICHE), Kumaramcoil, Thuckalay Kanyakumari,, Noorul Islam Centre for Higher Education (NICHE), Kumaramcoil, Thuckalay Kanyakumari, Tamilnadu,, Tamilnadu, India, India
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Abstract
nClimatic variations are a pressing global challenge that demands immediate and comprehensive attention. A wealth of articles has been published on climate change mitigation and adaptation, yet there remains a need for innovative methods to explore the complexities of climatic variations and to devise more efficient and effective strategies for adjustment and alleviation. With technological advancements, machine learning (ML) and deep learning (DL) approaches have derived significant popularity across various fields, including climatic variations research. The objective of this paper is to investigate the most prevalent ML and DL approaches implemented to climatic variations mitigation and adaptation. Additionally, it seeks to identify the utmost common mitigation and adaptation measures, with a particular focus on urban areas, that have been investigated utilizing machine learning and deep learning methodologies. To fulfill these aims, this study utilizes word frequency analysis and topic modelling, specifically employing the Latent Dirichlet Allocation (LDA) algorithm as a machine learning tool. The findings indicate that Artificial Neural Networks (ANNs) are the predominant ML technique in both climate change mitigation and adaptation endeavours. Moreover, within various research domains concerning climate change, geoengineering and investigations into land surface temperature stand out as the fields that have most extensively employed ML and DL algorithms.
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Keywords: Clustering, machine learning, greenhouse gas, finite-time thermodynamics, climate change.
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References
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- Diffenbaugh NS, Field CB. Changes in ecologically critical terrestrial climate conditions. Science. 2013 Aug 2;341(6145):486-92.
- David Rolnick, Priya L. Donti, Lynn H. Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, Alexandra Luccioni, Tegan Maharaj, Evan D. Sherwin, S. Karthik Mukkavilli, Konrad P. Kording, Carla Gomes, Andrew Y. Ng, Demis Hassabis, John C. Platt, Felix Creutzig, Jennifer Chayes, Yoshua Bengio. Tackling Climate Change with Machine Learning. Computer Science > Computers and Society. arXiv:1906.05433
- Chris Huntingford et al. Machine learning and artificial intelligence to aid climate change research and preparedness. 2019 Environ. Res. Lett. 14(12) 124007
- Tayebi AH, Sharifi R, Salemi AH, Faghihi F. Investigating the effect of different penetration of renewable energy resources on islanded microgrid frequency control using a robust method. Signal Processing and Renewable Energy. 2021 Jun 1;5(2):15-34.
- Nalau J, Verrall B. Mapping the evolution and current trends in climate change adaptation science. Climate Risk Management. 2021 Jan 1; 32: 100290.
- Koc M and Acar A (2021) Investigation of urban climates and built environment relations by using machine learning. Urban Climate 37(2021): 100820.
- Hermwille L, Obergassel W, Ott HE, Beuermann C. UNFCCC before and after Paris–what’s necessary for an effective climate regime? Climate policy. 2017 Feb 17;17(2):150-70.
- Nikola Milojevic-Dupont, Felix Creutzig. Machine learning for geographically differentiated climate change mitigation in urban areas. Sustainable Cities and Society. Volume 64, January 2021, 102526
- Reckien D, Salvia M, Heidrich O, Church JM, Pietrapertosa F, De Gregorio-Hurtado S, d’Alonzo V, Foley A, Simoes SG, Lorencová EK, Orru H. How are cities planning to respond to climate change? Assessment of local climate plans from 885 cities in the EU-28. Journal of cleaner production. 2018 Aug 1; 191: 207-219.
- Li SY, Shan M, Zhai Z. Understanding key determinants of health climate in building construction projects. Environmental Science and Pollution Research. 2023 Apr;30(18):51450-63.
- Abduljabbar R, Dia H, Liyanage S, et al. (2019) Applications of artificial intelligence in transport: an overview. Sustainability 11: 189.
- Faghmous JH, Kumar V. A big data guide to understanding climate change: The case for theory-guided data science. Big data. 2014 Sep 1;2(3):155-63.
- Nosratabadi S, Mosavi A, Shamshirband S, Zavadskas EK, Rakotonirainy A, Chau KW. Sustainable business models: A review. Sustainability. 2019 Mar 19;11(6):1663.
- Vapnik VN. An overview of statistical learning theory. IEEE transactions on neural networks. 1999 Sep;10(5):988-99.
- Audu ARA, Cuzzocrea A, Leung C, et al. (2020). An Intelligent Predictive Analytics System for Transportation Analytics on Open Data Towards the Development of a Smart City. Hussain F K, Barolli L and Ikeda M (eds). Springer Verlag, 224–236.
- Wataya E, Shaw R. Measuring the value and the role of soft assets in smart city development. Cities. 2019 Nov 1;94:106-15.
- Murphy KP. Machine learning: a probabilistic perspective. MIT press; 2012 Sep 7.
- Sun, A. Y., & Scanlon, B. R. (2019). How Can Big Data and Machine Learning Environment and Water Management: A Survey of Methods, Applications, and Future Directions. Environmental Research Letters, 14, Article ID: 073001. https://doi.org/10.1088/1748-9326/ab1b7d
- Ullah I, Youn HY. Efficient data aggregation with node clustering and extreme learning machine for WSN. The Journal of Supercomputing. 2020 Dec;76(12):10009-35.
- Dijkstra FA, Morgan JA, Follett RF, Lecain DR. Climate change reduces the net sink of CH4 and N2O in a semiarid grassland. Global Change Biology. 2013 Jun;19(6):1816-26.
- LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
- Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M. et al. Deep learning applications and challenges in big data analytics. Journal of Big Data 2, 1 (2015). https://doi.org/10.1186/s40537-014-0007-7
- Cheng H, Wang R, Zhang Z, et al (2019) Explaining decision-making algorithms through UI: strategies to help non-expert stakeholders. In: CHI ’19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, Glasgow Scotland Uk, May 2019. Paper No. 559. 1–12.
- Churkina G (2016) The role of urbanization in the global carbon cycle. Frontiers in Ecology and Evolution 3: 144. DOI: 10.3389/fevo.2015.00144.
- Dahal B, Kumar SAP and Li Z (2019) Topic modeling and sentiment analysis of global climate change tweets. Social Network Analysis and Mining 9: 24.
- Garcia DH (2021) Analysis and precision of the terrestrial surface temperature using landsat 8 and sentinel 3 images: study applied to the city of Granada (Spain). Sustainable Cities and Society 71: 102980. DOI: 10. 1016/j.scs.2021.102980.
- Albalawi R, Yeap TH and Benyoucef M (2020) Using topic modeling methods for short-text data: a comparative analysis. Front Artif Intell 3: 42.
- Anthony LFW, Kanding B and Selvan R (2020) Carbontracker: tracking and predicting the carbon footprint of training deep learning models. In: ICML Workshop on “Challenges in Deploying and Monitoring Machine Learning Systems, Vienna, Austria, 17-18 July 2020.
- Bacciu D, Micheli A and Sperduti A (2012) Compositional generative mapping for tree-structured data-part I: bottom-up probabilistic modeling of trees. IEEE Transactions on Neural Networks and Learning Systems 23: 1987–2002.
- Bardhan R, Debnath R, Gama J, et al. (2020) REST framework: a modelling approach towards cooling energy stress mitigation plans for future cities in warming Global South. Sustainable Cities and Society 61: 102315. DOI: 10.1016/j.scs.2020.102315.
- Bedsworth LW and Hanak E (2010) Adaptation to climate change. Journal of the American Planning Association 76(4): 477–495.
- Benites-Lazaro LL, Giatti L and Giarolla A (2018) Topic modeling method for analyzing social actor discourses on climate change, energy and food security. Energy Research & Social Science 45: 318–330.
- Getoor B and Taskar L (2007) Introduction to Statistical Relational Learning; Volume L of Adaptive Computation and Machine Learning. Cambridge, MA, USA: MIT Press.
- Kim I, Le Q, Park S, et al. (2014) Driving forces in archetypical land-use changes in a mountainous watershed in East Asia. Land 3(3): 957–980.
- Lackner M, Chen WY and Suzuki T (2015) Introduction to climate change mitigation. In: Chen WY, Suzuki T and Lackner M (eds), Handbook of Climate Change Mitigation and Adaptation. New York, NY: Springer.
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Research & Reviews : Journal of Space Science & Technology
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| Volume | 13 | |
| [if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] | 02 | |
| Received | July 5, 2024 | |
| Accepted | July 27, 2024 | |
| Published | August 16, 2024 |
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