Application of RBF artificial neural networks to precipitation and temperature forecasting in Paraná, Brazil
Keywords:Precipitation and temperature forecasting, Artificial neural network, Selection of input variables
Precipitation and temperature have an impact on various sectors of society, such as agriculture, power generation, water availability, so it is essential to develop accurate monthly forecasts. The objective of this study is to develop an artificial neural network (ANN) model for monthly temperature and precipitation forecasts for the state of Paraná, Brazil. An important step in the ANN model is the selection of input variables, for which the forward stepwise regression method was used. After identifying the predictor variables for the forecasting model, the Radial Basis Function (RBF) ANN was developed with 50 neurons in the hidden layer and one neuron in the output layer. For the precipitation forecasting models, better performances were obtained for forecasting the data smoothed by the three-month moving average, since noisy data, such as monthly precipitation, are more difficult to be simulated by the neural network. For the temperature forecasts, the ANN model performed well both in the monthly temperature forecast and in the 3-month moving average forecast. This study showed the suitability of forecasting precipitation and temperature with the use of RBF ANNs, especially in the forecast of the monthly temperature.
ABBOT, J., MAROHASY, J. Forecasting of monthly rainfall in the Murray Darling Basin, Australia: Miles as a case study. WIT Transactions on Ecology and the Environment. 2015; 197, 149-159.
ANOCHI, J. A., DE CAMPOS VELHO, H. F. Previsão climática de precipitação para a região Sul por rede neural autoconfigurada. Ciência e Natura. 2016; 38, 98-104.
BODRI, L., ČERMÁK, V. Prediction of extreme precipitation using a neural network: application to summer flood occurrence in Moravia. Advances in Engineering Software. 2000; 31(5), 311-321.
CERA, J. C. E FERRAZ, S. E. T. Variações climáticas na precipitação no sul do brasil no clima presente e futuro. Revista Brasileira de Meteorologia. 2015; 30(1)
COPERNICUS. Copernicus climate change service (c3s): Era5: Fifth generation of ecmwf atmospheric reanalyses of the global climate. 2017 [cited 2019 dec 08]. Available from: https://cds.climate.copernicus.eu/.
GHOLIZADEH, M. H., DARAND, M. Forecasting precipitation with artificial neural networks (case study: Tehran). Applied Sci. 2009; 9, 1786-1790.
GRIMM, A. M. Clima da região Sul do Brasil. Tempo e Clima no Brasil. São Paulo: Oficina de Textos. 2009; 259-275.
GRIMM, A. M., FERRAZ, S. E. E GOMES, J. Precipitation anomalies in southern brazil associated with el niño and la niña events. Journal of climate. 1998; 11(11):2863–2880.
HE, H., YAN, Y., CHEN, T., CHENG, P. Tree Height Estimation of Forest Plantation in Mountainous Terrain from Bare-Earth Points Using a DoG-Coupled Radial Basis Function Neural Network. Remote Sensing. 2019; 11(11), 1271.
LEE, J., KIM, C. G., LEE, J., KIM, N., KIM, H. Application of artificial neural networks to rainfall forecasting in the Geum River basin, Korea. Water. 2018; 10(10), 1448.
LIU, Q., ZOU, Y., LIU, X., LINGE, N. A survey on rainfall forecasting using artificial neural network. IJES. 2019; 11(2), 240-249.
MAHESHWARI, V. Dimensionality reduction techniques. 2019 [cited in 2019 jun 11]. Available from: https://medium.com/datadriveninvestor/dimensionality-reduction-techniques-27049b5a4c55.
NEZHAD, E. F., GHALHARI, G. F., & BAYATANI, F. Forecasting Maximum Seasonal Temperature Using Artificial Neural Networks “Tehran Case Study”. Asia-Pacific Journal of Atmospheric Sciences. 2019; 55(2), 145-153.
NIMER, E. Climatologia do brasil. IBGE; 1989.
REBOITA, M. S., DIAS, C. G., DUTRA, L. M. M., & LLOPART, M. Previsão climática sazonal para o Brasil obtida através de modelos climáticos globais e regional. Rev. Bras. Meteorol. 2018; 0102-7786332001.
SILVA, I. D., SPATTI, D. H. E FLAUZINO, R. A. Redes neurais artificiais para engenharia e ciências aplicadas. 2010; 23.
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