Application of RBF artificial neural networks to precipitation and temperature forecasting in Paraná, Brazil

Authors

DOI:

https://doi.org/10.5902/2179460X43258

Keywords:

Precipitation and temperature forecasting, Artificial neural network, Selection of input variables

Abstract

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.

Downloads

Download data is not yet available.

Author Biographies

Carla Milléo, Universidade Federal do Paraná, Curitiba, PR

Possui graduação em Engenharia Ambiental. Atualmente é residente técnica em engenharia ambiental no Instituto Água e Terra e, além disso, é mestranda em Engenharia Ambiental pela mesma instituição.

Ricardo Carvalho de Almeida, Universidade Federal do Paraná, Curitiba, PR

Doutor na área de concentração de Ciências Atmosféricas em Engenharia, pela Coordenação de Programas de Pós-Graduação em Engenharia, da Universidade Federal do Rio de Janeiro e Professor Associado do Departamento de Engenharia Ambiental, da Universidade Federal do Paraná.

References

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.

Downloads

Published

2021-03-01

How to Cite

Milléo, C., & Almeida, R. C. de. (2021). Application of RBF artificial neural networks to precipitation and temperature forecasting in Paraná, Brazil. Ciência E Natura, 43, e40. https://doi.org/10.5902/2179460X43258

Issue

Vol. 43 (2021): Continuous Publication

Section

Meteorology

License

Copyright (c) 2021 Ciência e Natura

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

To access the DECLARATION AND TRANSFER OF COPYRIGHT AUTHOR’S DECLARATION AND COPYRIGHT LICENSE click here.

 

Ethical Guidelines for Journal Publication

The Ciência e Natura journal is committed to ensuring ethics in publication and quality of articles. 

Conformance to standards of ethical behavior is therefore expected of all parties involved: Authors, Editors, Reviewers, and the Publisher.

In particular, 

Authors: Authors should present an objective discussion of the significance of research work as well as sufficient detail and references to permit others to replicate the experiments. Fraudulent or knowingly inaccurate statements constitute unethical behavior and are unacceptable. Review Articles should also be objective, comprehensive, and accurate accounts of the state of the art. The Authors should ensure that their work is entirely original works, and if the work and/or words of others have been used, this has been appropriately acknowledged. Plagiarism in all its forms constitutes unethical publishing behavior and is unacceptable. Submitting the same manuscript to more than one journal concurrently constitutes unethical publishing behavior and is unacceptable. Authors should not submit articles describing essentially the same research to more than one journal. The corresponding Author should ensure that there is a full consensus of all Co-authors in approving the final version of the paper and its submission for publication.

Editors: Editors should evaluate manuscripts exclusively on the basis of their academic merit. An Editor must not use unpublished information in the editor's own research without the express written consent of the Author. Editors should take reasonable responsive measures when ethical complaints have been presented concerning a submitted manuscript or published paper.

Reviewers: Any manuscripts received for review must be treated as confidential documents. Privileged information or ideas obtained through peer review must be kept confidential and not used for personal advantage. Reviewers should be conducted objectively, and observations should be formulated clearly with supporting arguments, so that Authors can use them for improving the paper. Any selected Reviewer who feels unqualified to review the research reported in a manuscript or knows that its prompt review will be impossible should notify the Editor and excuse himself from the review process. Reviewers should not consider manuscripts in which they have conflicts of interest resulting from competitive, collaborative, or other relationships or connections with any of the authors, companies, or institutions connected to the papers.