A machine learning and statistical learning-based pipeline to perform multipoint rainfall forecasting
Resumo
Analisar e prever a precipitação é crucial para a sociedade, principalmente quando ocorrem chuvas extremas e inundações. Tais eventos impactam as estruturas socioeconômicas e podem levar a fatalidades. O entendimento da formação das chuvas e das variáveis associadas auxilia no desenvolvimento de modelos preditivos dos níveis de precipitação, auxiliando na tomada de decisões nas cadeias produtivas e na mobilidade urbana. Avanços recentes neste campo resultam do aumento do poder de processamento do computador e da disponibilidade de dados meteorológicos em todo o mundo. Este estudo se concentra na avaliação dos modelos SARIMA, RNN e XGBoost para prever a precipitação mensal ao longo de uma ferrovia no norte do Brasil. Usando valores de precipitação defasados, SARIMA teve melhor desempenho em 11 dos 13 pontos (com R² variando de 0,704 a 0,817), enquanto RNN superou nos pontos restantes (15,39% dos pontos avaliados).
Palavras-chave:
Machine Learning, Statistical Learning, Rainfall Forecast, Preciptation
Referências
Berrang-Ford, L., Sietsma, A. J., Callaghan, M., Minx, J. C., Scheelbeek, P. F., Haddaway, N. R., Haines, A., and Dangour, A. D. (2021). Systematic mapping of global research on climate and health: a machine learning review. The Lancet Planetary Health, 5(8):e514–e525.
Cerveny, R. S., Bessemoulin, P., Burt, C. C., Cooper, M. A., Cunjie, Z., Dewan, A., Finch, J., Holle, R. L., Kalkstein, L., Kruger, A., et al. (2017). Wmo assessment of weather and climate mortality extremes: lightning, tropical cyclones, tornadoes, and hail. Weather, climate, and society, 9(3):487–497.
Chhetri, M., Kumar, S., Pratim Roy, P., and Kim, B.-G. (2020). Deep blstm-gru model for monthly rainfall prediction: A case study of simtokha, bhutan. Remote sensing, 12(19):3174.
Fang, J., Li, M., and Shi, P. (2015). Mapping flood risk of the world. World Atlas of Natural Disaster Risk, pages 69–102.
Fernandes, E., Rocha, R. L., Ferreira, B., Carvalho, E., Siravenha, A. C., Gomes, A. C. S., Carvalho, S., and de Souza, C. R. (2018). An ensemble of convolutional neural networks for unbalanced datasets: A case study with wagon component inspection. In 2018 International Joint Conference on Neural Networks (IJCNN), pages 1–6. IEEE.
Group, R. S. Re swiss group. http://bit.ly/SwissReFlood. Accessed: 2023-04-18.
McGuffie, K. and Henderson-Sellers, A. (2001). Forty years of numerical climate modelling. International Journal of Climatology: A Journal of the Royal Meteorological Society, 21(9):1067–1109.
Moazzam, M. F. U., Rahman, G., Munawar, S., Tariq, A., Safdar, Q., and Lee, B.-G. (2022). Trends of rainfall variability and drought monitoring using standardized precipitation index in a scarcely gauged basin of northern pakistan. Water, 14(7):1132.
Monego, V. S., Anochi, J. A., and de Campos Velho, H. F. (2022). South america seasonal precipitation prediction by gradient-boosting machine-learning approach. Atmosphere, 13(2):243.
NWS. Nws preliminary us flood fatality statistics. https://www.weather.gov/arx/usflood. Accessed: 2023-04-18.
Qerimi, Q. and Sergi, B. S. (2022). The case for global regulation of carbon capture and storage and artificial intelligence for climate change. International Journal of Greenhouse Gas Control, 120:103757.
Rocha, R. L., Silva, C. D., Gomes, A. C. S., Ferreira, B. V., Carvalho, E. C., Siravenha, A. C. Q., and Carvalho, S. R. (2019). Image inspection of railcar structural components: An approach through deep learning and discrete fourier transform. In Anais do VII Symposium on Knowledge Discovery, Mining and Learning, pages 33–40. SBC.
Santos, R. S. and Qin, L. (2019). Risk capital and emerging technologies: innovation and investment patterns based on artificial intelligence patent data analysis. Journal of Risk and Financial Management, 12(4):189.
Su, B., Xiao, C., Zhao, H., Huang, Y., Dou, T., Wang, X., and Chen, D. (2022). Estimated changes in different forms of precipitation (snow, sleet, and rain) across china: 1961–2016. Atmospheric Research, 270:106078.
Yin, K., Cai, F., and Huang, C. (2022). How does artificial intelligence development affect green technology innovation in china? evidence from dynamic panel data analysis. Environmental Science and Pollution Research, pages 1–25.
Zhou, Z., Ren, J., He, X., and Liu, S. (2021). A comparative study of extensive machine learning models for predicting long-term monthly rainfall with an ensemble of climatic and meteorological predictors. Hydrological Processes, 35(11):e14424.
Cerveny, R. S., Bessemoulin, P., Burt, C. C., Cooper, M. A., Cunjie, Z., Dewan, A., Finch, J., Holle, R. L., Kalkstein, L., Kruger, A., et al. (2017). Wmo assessment of weather and climate mortality extremes: lightning, tropical cyclones, tornadoes, and hail. Weather, climate, and society, 9(3):487–497.
Chhetri, M., Kumar, S., Pratim Roy, P., and Kim, B.-G. (2020). Deep blstm-gru model for monthly rainfall prediction: A case study of simtokha, bhutan. Remote sensing, 12(19):3174.
Fang, J., Li, M., and Shi, P. (2015). Mapping flood risk of the world. World Atlas of Natural Disaster Risk, pages 69–102.
Fernandes, E., Rocha, R. L., Ferreira, B., Carvalho, E., Siravenha, A. C., Gomes, A. C. S., Carvalho, S., and de Souza, C. R. (2018). An ensemble of convolutional neural networks for unbalanced datasets: A case study with wagon component inspection. In 2018 International Joint Conference on Neural Networks (IJCNN), pages 1–6. IEEE.
Group, R. S. Re swiss group. http://bit.ly/SwissReFlood. Accessed: 2023-04-18.
McGuffie, K. and Henderson-Sellers, A. (2001). Forty years of numerical climate modelling. International Journal of Climatology: A Journal of the Royal Meteorological Society, 21(9):1067–1109.
Moazzam, M. F. U., Rahman, G., Munawar, S., Tariq, A., Safdar, Q., and Lee, B.-G. (2022). Trends of rainfall variability and drought monitoring using standardized precipitation index in a scarcely gauged basin of northern pakistan. Water, 14(7):1132.
Monego, V. S., Anochi, J. A., and de Campos Velho, H. F. (2022). South america seasonal precipitation prediction by gradient-boosting machine-learning approach. Atmosphere, 13(2):243.
NWS. Nws preliminary us flood fatality statistics. https://www.weather.gov/arx/usflood. Accessed: 2023-04-18.
Qerimi, Q. and Sergi, B. S. (2022). The case for global regulation of carbon capture and storage and artificial intelligence for climate change. International Journal of Greenhouse Gas Control, 120:103757.
Rocha, R. L., Silva, C. D., Gomes, A. C. S., Ferreira, B. V., Carvalho, E. C., Siravenha, A. C. Q., and Carvalho, S. R. (2019). Image inspection of railcar structural components: An approach through deep learning and discrete fourier transform. In Anais do VII Symposium on Knowledge Discovery, Mining and Learning, pages 33–40. SBC.
Santos, R. S. and Qin, L. (2019). Risk capital and emerging technologies: innovation and investment patterns based on artificial intelligence patent data analysis. Journal of Risk and Financial Management, 12(4):189.
Su, B., Xiao, C., Zhao, H., Huang, Y., Dou, T., Wang, X., and Chen, D. (2022). Estimated changes in different forms of precipitation (snow, sleet, and rain) across china: 1961–2016. Atmospheric Research, 270:106078.
Yin, K., Cai, F., and Huang, C. (2022). How does artificial intelligence development affect green technology innovation in china? evidence from dynamic panel data analysis. Environmental Science and Pollution Research, pages 1–25.
Zhou, Z., Ren, J., He, X., and Liu, S. (2021). A comparative study of extensive machine learning models for predicting long-term monthly rainfall with an ensemble of climatic and meteorological predictors. Hydrological Processes, 35(11):e14424.
Publicado
25/09/2023
Como Citar
CARVALHO, Eduardo; OLIVEIRA, Ewerton; ROCHA, Rafael; CARNEIRO, Nikolas; TEDESCHI, Renata; ALVES, Ronnie.
A machine learning and statistical learning-based pipeline to perform multipoint rainfall forecasting. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 726-740.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2023.234376.
