Use of artificial intelligence for seasonal precipitation forecasting in the Minas Gerais state, Brazil
DOI:
https://doi.org/10.5902/2179460X90228Keywords:
Artificial intelligence, Seasonal precipitation, Forecast modelsAbstract
Precipitation forecast is crucial for water resource management, agriculture, and climate disaster mitigation. This study investigates the use of artificial intelligence (AI) to predict seasonal precipitation in the state of Minas Gerais, Brazil. Utilizing neural networks trained on high temporal and spatial resolution datasets (GPCC, GPCP, MERGE-CPTEC, and CHIRPS), the research compares AI-based forecasts with numerical physics-based models. The results indicate that averaging multiple runs of each neural network model and using higher spatial resolution datasets, particularly CHIRPS, yield more accurate predictions, with lower Root Mean Square Errors (RMSE) and Relative Errors (RE). AI applications have shown promising results, offering forecasts with reduced computational processing time compared to traditional models. The study emphasizes the need to enhance computational infrastructure and explores the potential of advanced AI techniques to improve extreme climatic events forecasts, benefiting sectors such as agriculture and water resource management.
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Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., & Nelkin, E. (2003). The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present). Journal of hydrometeorology, 4(6), 1147-1167.
Amante, C., & Eakins, B. W. (2009). ETOPO1 - 1 arc-minute global relief model: procedures, data sources and analysis. (NOAA Technical Memorandum NESDIS NGDC-24). National Geophysical Data Center, National Oceanic and Atmospheric Administration. https://doi.org/10.7289/V5C8276M
Anochi, J. A., de Almeida, V. A., de Campos Velho, H. F. (2021). Machine learning for climate precipitation prediction modeling over South America. Remote Sensing, 13(13), 2468. https://doi.org/10.3390/rs13132468
Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)? Geoscientific model development discussions, 7(1), 1525-1534. https://doi.org/10.5194/gmd-7-1247-2014
Chen, L., Zhong, X., Li, H., Wu, J., Lu, Bo., Chen, D., Xie, S., Wu, L., Chao, Q., Lin, C., & Qi, Y. (2024). A machine learning model that outperforms conventional global subseasonal forecast models. Nature Communications, 15(1), 6425. https://doi.org/10.1038/s41467-024-50714-1
Conti, S. (2024). Artificial intelligence for weather forecasting. Nature Reviews Electrical Engineering,1 (1), 8-8. https://doi.org/ 10.1038/s44287-023-00009-2
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Scientific data, 2(1), 1-21. https://doi.org/10.1038/sdata.2015.66
Huffman, G. J., Adler, R. F.; Behrangi, Ali; Bolvin, David T.; Nelkin, Eric; Song, Yang. Algorithm Theoretical Basis Document (ATBD) for Global Precipitation Climatology Project Version 3.1 Precipitation Data. 2020. Disponível em: https://disc.gsfc.nasa.gov/datasets/GPCPMON_3.2/summary. Acesso em: 11 mar. 2023.
Instituto Brasileiro de Geografia e Estatística (2022). Área territorial brasileira 2022. https://cidades.ibge.gov.br/brasil/mg/panorama. Acesso em: 05 nov. 2024.
Kirtman, B. P., Min, D., Infanti, J. M., Kinter, J. L., Paolino, D. A., Zhang. Q., van den Dool, H., Saha, S., Mendez, M. P., Becker, E., Peng, P., Tripp, P., Huang, P., DeWitt, D. G., Barnston, A. G., Li, S., Rosati, A., Schubert, S. D., et al. (2014). The North American multimodel ensemble: phase-1 seasonal-to-interannual prediction; phase-2 toward developing intraseasonal prediction. Bulletin of the American Meteorological Society, 95(4), 585-601. https://doi.org/10.1175/BAMS-D-12-00050.1
Lam, R., Gonzalez-Sanchez, A., Willson. M., Wirnsberger, P., Fortunato, M., Alet, F., Ravuri, S., Ewalds, T., Eaton-Rosen, Z., Hu, W., Merose, A., Hoyer, S., Holland, G., Vinyals, O., Stott, J., Prtizel, A., Mohamed, S., & Battgalia, P. (2023). Learning skillful medium-range global weather forecasting. Science, 382(6677), 1416-1421. https://doi.org/ 10.1126/science.adi2336
Marengo, J. A., Seluchi, M. E., Cunha, A. P., Cuartas, L. A., Goncalves, D., Sperling, V. B., ... & Moraes, O. L. (2023). Heavy rainfall associated with floods in southeastern Brazil in November–December 2021. Natural Hazards, 116(3), 3617-3644. https://doi.org/10.1007/s11069-023-05827-z
Marmolin, H. Subjective MSE measures. IEEE transactions on systems, man, and cybernetics, 16(3), 486-489. https://doi.org/10.1109/TSMC.1986.4308985
Materia, S., García, L. P., van Straaten, C., O, S., Mamalakis, A., Cavicchia, L., Coumou, D., de Luca, P., Kretschmer, M., & Donat, M. (2024). Artificial intelligence for climate prediction of extremes: State of the art, challenges, and future perspectives. WIREs Climate Change, 15(6), e914. https://doi.org/10.1002/wcc.914
Minuzzi, R. B., Sediyama, G. C., Ribeiro, A., & da Costa, J. M. N. (2005). El Niño: ocorrência e duração dos veranicos do Estado de Minas Gerais. Revista brasileira de engenharia agrícola e ambiental, 9, 364-371. https://doi.org/10.1590/S1415-43662005000300011
Rizzo, I. V., & Canato, R. L. C. (2020). Inteligência artificial: funções de ativação. Prospectus, 2(2), 51–65.
Rozante, J. R., Moreira, D. S., de Gonçalves, L. G. G., Vila, D. A. (2010). Combining TRMM and surface observations of precipitation: technique and validation over South America. Weather and forecasting, 25(3), 885-894. https://doi.org/10.1175/2010WAF2222325.1
Shimizu, M. H., Ambrizzi, T., & Liebmann, B. (2017). Extreme precipitation events and their relationship with ENSO and MJO phases over northern South America. International Journal of Climatology, 37(6), 2977-2989. https://doi.org/10.1002/joc.4893
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