Beyond Accuracy: A Comparative Study of Machine Learning Models for Extreme Weather Forecasting in Rio de Janeiro with a Green AI Perspective

• Gabriel B. Breder UFF
• Gyslla Vasconcelos UFF
• Mariza Ferro UFF

DOI: https://doi.org/10.5753/eniac.2025.14418

Abstract

In recent years, climate change has severely impacted the city of Rio de Janeiro. Some extreme events have occurred, such as temperatures above 40° C and heavy rains that have caused landslides, floods, and deaths. This study investigates the application of Machine Learning (ML) models (MLP, XGBoost, and LSTM) to predict extreme temperature and precipitation for the city of Rio de Janeiro, while critically evaluating their environmental impact through a Green AI perspective. Beyond traditional accuracy metrics like RMSE, our aim is to assess the trade-off between good prediction and computational efficiency, energy consumption, CO2 emissions, and water usage during model training.

References

Allen, A., Markou, S., Tebbutt, W., Requeima, J., Bruinsma, W. P., Andersson, T. R., Herzog, M., Lane, N. D., Chantry, M., Hosking, J. S., and Turner, R. E. (2025). End-to-end data-driven weather prediction. Nature Communications.

Anwar, M. T., Winarno, E., Hadikurniawati, W., and Novita, M. (2021). Rainfall prediction using extreme gradient boosting. Journal of Physics: Conference Series, 1869(1):012078.

Ayzel, G., Scheffer, T., and Heistermann, M. (2020). Rainnet v1.0: A convolutional neural network for radar-based precipitation nowcasting. Geoscientific Model Development, 13:2631–2644.

Bendre, N., Marn, H. T., and Najafirad, P. (2020). Learning from few samples: A survey. arXiv.

Bouallègue, Z. B. et al. (2024). The rise of data-driven weather forecasting: A first statistical assessment of machine learning–based weather forecasts in an operational-like context. Bulletin of the American Meteorological Society, 105(6):E864–E883.

Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16. ACM.

Cowls, J., Tsamados, A., Taddeo, M., and Floridi, L. (2021). The ai gambit: leveraging artificial intelligence to combat climate change—opportunities, challenges, and recommendations. Ai & Society, pages 1 – 25.

da Cunha, F. P., Manso, M. C., and Júnior, A. A. O. (2024). Analysis of the occurrence of extreme events in the city of rio de janeiro/rj. Revista de Gestão Social e Ambiental, 18(4):e04730.

da Silva, F. P., da Silva, A. S., and da Silva, M. G. A. J. (2022a). Extreme rainfall events in the rio de janeiro city (brazil): description and a numerical sensitivity case study. Meteorology and Atmospheric Physics, 134:1–20.

da Silva, F. P., da Silva, A. S., and da Silva, M. G. A. J. (2022b). Extreme rainfall events in the rio de janeiro city (brazil): description and a numerical sensitivity case study. Meteorology and Atmospheric Physics, 134(77).

da Silva, F. P., da Silva, M. G. A. J., Filho, O. C. R., et al. (2020). Pattern evaluation of operational radar reflectivity–rainfall relationship for quitandinha river flooding events: Petrópolis, rio de janeiro (brazil). Environmental Earth Sciences, 79:525.

Dereczynski, C. P. and Calado, Renata Novaes e Bodstein de Barros, A. (2017). Chuvas extremas no município do rio de janeiro: Histórico a partir do século xix. Anuário do Instituto de Geociências - UFRJ, 40(2):17–30.

Han, D., Im, J., Shin, Y., and Lee, J. (2023). Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning. Geoscientific Model Development, 16(20):5895–5914.

Intergovernmental Panel on Climate Change (IPCC) (2021). Climate Change 2021 – The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge.

Kumar, R., Goel, R., Sidana, N., Sharma, A., ghai, S., Singh, T., singh, R., Priyadarshi, N., Twala, B., and Ahmad, V. (2024). Enhancing climate forecasting with ai: Current state and future prospect. F1000Research, 13(1094).

Lamptey, B., Sahabi ABED, S., Gudoshava, M., Mutemi, J., Bopape, M.-J., Adefisan, E., Igri, P., Sanda, S., Ndiaye, O., Parker, D., Dougill, A., Fink, A., Knippertz, P., Woolnough, S., and Kolstad, E. (2024). Challenges and ways forward for sustainable weather and climate services in africa. Nature Communications, 15:4.

Li, P., Yang, J., Islam, M. A., and Ren, S. (2025). Making ai less ”thirsty”: Uncovering and addressing the secret water footprint of ai models.

Ma, X., Fang, C., and Ji, J. (2020). Prediction of outdoor air temperature and humidity using xgboost. IOP Conference Series: Earth and Environmental Science, 427:012013.

Mfetoum, I. M., Ngoh, S. K., Molu, R. J. J., Nde Kenfack, B. F., Onguene, R., Naoussi, S. R. D., Tamba, J. G., Bajaj, M., and Berhanu, M. (2024). A multilayer perceptron neural network approach for optimizing solar irradiance forecasting in central africa with meteorological insights. Scientific Reports, 14(1):3572.

Polifke, F., Filho, O. C. R., da Silva, M. G. A. J., et al. (2020). Observed and estimated atmospheric thermodynamic instability using radiosonde observations over the city of rio de janeiro, brazil. Meteorology and Atmospheric Physics, 132:297–314.

Porto, F., Ferro, M., Ogasawara, E., Moeda, T., de Barros, C. D. T., Silva, A. C., Zorrilla, R., Pereira, R. S., Castro, R. N., Silva, J. V., Salles, R., Fonseca, A. J., Hermsdorff, J., Magalhães, M., Sá, V., Simões, A. A., Cardoso, C., and Bezerra, E. (2022). Machine learning approaches to extreme weather events forecast in urban areas: Challenges and initial results. Supercomputing Frontiers and Innovations, 9(1):49–73.

Regoto, P., Dereczynski, C., Chou, S. C., and Bazzanela, A. C. (2021). Observed changes in air temperature and precipitation extremes over brazil. International Journal of Climatology, 41(11):5125–5142.

Reig, P., Luo, T., Christensen, E., and Sinistore, J. C. (2020). Guidance for calculating water use embedded in purchased electricity.

report, U. I. I. (2024). Uptime institute global data center survey results 2024.

Rosenblatt, F. (1958). The perceptron: a probabilistic model for information storage and organization in the brain. Psychological review, 65 6:386–408.

Sharma, H., Hegde, N., Banerjee, I., Bhattacharyya, A., and Srivastava, P. (2024). Climate-induced migration in the global south: an in-depth analysis. npj Urban Sustainability, 4:133.

Sharma, O., Trivedi, D., Pattnaik, S., Hazra, V., and Puhan, N. B. (2023). Improvement in district scale heavy rainfall prediction over complex terrain of north east india using deep learning. IEEE Transactions on Geoscience and Remote Sensing, 61:1–8.

Shi, X., Chen, Z., Wang, H., et al. (2015). Convolutional lstm network: A machine learning approach for precipitation nowcasting. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 1, pages 802–810, Cambridge, MA, USA. MIT Press.

Sonderby, C. K., Espeholt, L., Heek, J., Dehghani, M., Oliver, A., Salimans, T., Agrawal, S., Hickey, J., and Kalchbrenner, N. (2020). Metnet: A neural weather model for precipitation forecasting. arXiv preprint arXiv:2003.12140. Accessed: 6 Feb. 2025.

Sætra, H. S. (2021). Ai in context and the sustainable development goals: Factoring in the unsustainability of the sociotechnical system. Sustainability, 13(4).

Vinuesa, R., Azizpour, H., Leite, I., Balaam, M., Dignum, V., Domisch, S., Felländer, A., Langhans, S., Tegmark, M., and Nerini, F. F. (2020). The role of AI in achieving the Sustainable Development Goals. Nature Communications, 11(233).

Xu, J., Wang, Z., Li, X., Li, Z., and Li, Z. (2024). Prediction of daily climate using long Short-Term memory (LSTM) model. Int. J. Innov. Sci. Res. Technol., pages 83–90.