Machine Learning Based Toolbox in Foreign Language for Children to Address Climate Change Adaptation

Authors

  • Zuleima Nuñez Leguia, Genis Arteaga Requena, Jhon Anaya Herrera, Nini Johana Villamizar Parada, Ligia Rosa Martinez Bula, Mario Alfonso Gándara Molina

Keywords:

Machine learning, k-nearest neighbour, decision tree, climate change, toolbox

Abstract

Climate change poses a significant threat to our planet, and educating future generations about its implications and solutions is paramount for effective adaptation and mitigation efforts. However, language barriers can hinder the dissemination of crucial information, particularly to children who may not yet be proficient in the predominant language of scientific discourse. This paper proposes a novel approach to addressing this challenge by developing a machine learning-based toolbox in a foreign language tailored for children. Leveraging advances in natural language processing and educational technology, the toolbox aims to facilitate interactive learning experiences in foreign languages, fostering a deeper understanding of climate change and promoting actionable strategies for adaptation. Machine learning algorithms like k-nearest neighbor, decision tree, logistic regression, and deep learning techniques such as natural language processing and artificial neural networks are being utilized to tackle climate change challenges across different sectors, including transportation

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References

Sietsma, A., Ford, J., Minx, J. (2023). "The next generation of machine learning for tracking adaptation texts." Nature Climate Change.

Gill, D. A., Blythe, J., Bennett, N., Evans, L., Brown, K., Turner, R. A., Baggio, J. A., Baker, D., Ban, N. C., Brun, V., Claudet, J., Darling, E., Di Franco, A., Epstein, G., Estradivari, N. J., Gray, G. G., Gurney, R. P., Horan, S. D., Jupiter, J. D., Muthiga, N. A. (2023). Triple exposure: Reducing negative impacts of climate change, blue growth, and conservation on coastal communities. One Earth, 6, 118–130.

Ingole, K., & Padole, D. (2023). Design Approaches for Internet of Things Based System Model for Agricultural Applications. In 11th International Conference on Emerging Trends in Engineering & Technology - Signal and Information Processing (ICETET - SIP)

Griffin, C., Wreford, A., & Cradock-Henry, N. A. (2023). ‘As a farmer you’ve just got to learn to cope’: Understanding dairy farmers’ perceptions of climate change and adaptation decisions in the lower South Island of Aotearoa-New Zealand. Journal of Rural Studies, 98, 147–158.

Guo, W., Qureshi, N. M. F., Jarwar, M. A., Kim, J., & Shin, D. R. (2023). AI-oriented Smart Power System Transient Stability: The Rationality, Applications, Challenges and Future Opportunities. Sustainable Energy Technologies and Assessments, 56, 102990.

Alassery, F., Alzahrani, A., Khan, A. I., Irshad, K., & Islam, S. (2022). An artificial intelligence-based solar radiation prophesy model for green energy utilization in energy management system. Sustainable Energy Technologies and Assessments, 52, 102060.

Anthopoulos, L., & Kazantzi, V. (2022). Urban energy efficiency assessment models from an AI and big data perspective: Tools for policy makers. Sustainable Cities and Society, 76, 103492.

ClimateChange.AI. (2022). "Using Machine Learning to Track International Climate Finance." Available at: https://www.climatechange.ai/blog/2022-11-09-climate-finance

Argyroudis, S. A., Mitoulis, S. A., Chatzi, E., Baker, J. W., Brilakis, I., Gkoumas, K., Vousdoukas, M., Hynes, W., Carluccio, S., Keou, O., Frangopol, D. M., & Linkov, I. (2022). Digital technologies can enhance climate resilience of critical infrastructure. Climate Risk Management, 35, 100387.

Aruta, J. J., Benzon, R., & Guinto, R. R. (2022). Safeguarding youth health in climate-vulnerable countries. The Lancet Child & Adolescent Health, 6, 223–224.

Azcona, F., Hakna, M. A., Mesa-Jurado, A.-T., Perera, M. Á. D., Mendoza-Carranza, M., Olivera-Villarroel, M., & Gloria de las Mercedes Gómez-Pais. (2022). Coastal communities’ adaptive capacity to climate change: Pantanos de Centla Biosphere Reserve, Mexico. Ocean & Coastal Management, 220, 106080

Bartmann, M. (2022). The Ethics of AI-Powered Climate Nudging—How Much AI Should We Use to Save the Planet? Sustainability., 14(9), 5153.

Bhagat, S. K., Tiyasha, T., Kumar, A., Malik, T., Jawad, A. H., Khedher, K. M., Deo, R. C., & Yaseen, Z. M. (2022). Integrative artificial intelligence models for Australian coastal sediment lead prediction: An investigation of in-situ measurements and meteorological parameters effects. Journal of Environmental Management, 309, 114711.

Dorward, A., & Giller, K. E. (2022). Change in the climate and other factors affecting agriculture, food or poverty: An opportunity, a threat or both? A personal perspective. Global Food Security, 33, 100623.

Filho, L., Walter, T. W., Mucova, S. A. R., Nagy, G. J., Balogun, A.-L., Luetz, J. M., Ng, A. W., Kovaleva, M., Azam, F. M. S., Alves, F., Guevara, Z., Matandirotya, N. R., Skouloudis, A., Tzachor, A., Malakar, K., & Gandhi, O. (2022). Deploying artificial intelligence for climate change adaptation. Technological Forecasting and Social Change, 180, 121662.

Fioravanti, C., Guarino, S., Mazzá, B., Nobili, M., Santucci, F., & Ansaldi, S. M. (2022). A Risk Assessment Framework for Critical Infrastructure Based on the Analytic Hierarchy Process. IFAC-PapersOnLine, 55, 277–282.

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Published

26.03.2024

How to Cite

Mario Alfonso Gándara Molina, Z. N. L. G. A. R. J. A. H. N. J. V. P. L. R. M. B. . (2024). Machine Learning Based Toolbox in Foreign Language for Children to Address Climate Change Adaptation. International Journal of Intelligent Systems and Applications in Engineering, 12(21s), 1581–1586. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5631

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Section

Research Article