Uso de ciência de dados para predição do consumo de fertilizantes no Brasil
Resumo
Fertilizantes sao elementos críticos para a produção de alimentos. Prever eficientemente como o consumo de fertilizantes ira se comportar nos próximos anos permite planejar adequadamente o aumento da produção e, com isso, mitigar problemas ambientais decorrentes de tal aumento de produção. Tendo em vista os elementos citados, esta pesquisa explora abordagens de ciência de dados para capacitar a predição do consumo de fertilizantes, atraves daotimização da construção de modelos. Os resultados indicam que o uso das ferramentas analíticas aqui apresentadas pode vir a ser uma maneira de obter previsoes confiáveis para planejar demandas futuras.
Palavras-chave:
fertilizantes, análise de dados, predição, aprendizado de máquina, pré-processamento de dados
Referências
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Coulibaly, P., Anctil, F., and Bobee, B. (2001). Multivariate reservoir inflow forecasting using temporal neural networks. Journal of Hydrologic Engineering, 6(5):367–376.
Deadman, D. and Ghatak, S. (1979). Forecasting fertilizer consumption and production: Longand short-run models. World Development, 7(11-12):1063–1072.
Dobermann, A. and Cassman, K. (2005). Cereal area and nitrogen use efficiency are drivers of future nitrogen fertilizer consumption. Science in China. Series C, Life sciences / Chinese Academy of Sciences, 48 Spec No:745–758.
FAO (2019). Food and agriculture organization of the united nations. Technical report, http://www.fao.org.
Gilland, B. (1993). Cereals, nitrogen and population: an assessment of the global trends. Endeavour, 17(2):84–88.
Han, J., Kamber, M., and Pei, J. (2011). Data Mining: Concepts and Techniques. Morgan Kaufmann, Haryana, India; Burlington, MA, 3 edition.
Howarth, R., Boyer, E., Pabich, W., and Galloway, J. (2002). Nitrogen use in the United States from 1961-2000 and potential future trends. Ambio, 31(2):88–96.
Hyndman, R. and Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(3):1–22.
Hyndman, R. J. and Koehler, A. B. (2006). Another look at measures of forecast accuracy. International journal of forecasting, 22(4):679–688.
Ogasawara, E., De Oliveira, D., Paschoal Jr., F., Castaneda, R., Amorim, M., Mauro, R., Soares, J., Quadros, J., and Bezerra, E. (2013). A forecasting method for fertilizers consumption in Brazil. International Journal of Agricultural and Environmental Information Systems, 4(2):23–36.
Ogasawara, E., Martinez, L., De Oliveira, D., Zimbrao, G., Pappa, G., and Mattoso, M. (2010). ˜Adaptive Normalization: A novel data normalization approach for non-stationary time series. In Proceedings of the International Joint Conference on Neural Networks.
Palmer, D., O’Boyle, N., Glen, R., and Mitchell, J. (2007). Random forest models to predict aqueous solubility. Journal of Chemical Information and Modeling, 47(1):150–158.
Pires, M., Da Cunha, D., De Matos Carlos, S., and Costa, M. (2015). Nitrogen-use efficiency, nitrous oxide emissions, and cereal production in Brazil: Current trends and forecasts. PLoS ONE, 10(8).
Salles, R., Belloze, K., Porto, F., Gonzalez, P., and Ogasawara, E. (2019). Nonstationary time series transformation methods: An experimental review. Knowledge-Based Systems, 164:274–291.
Salles, R., Bezerra, E., Soares, J., and Ogasawara, E. (2015). Evaluating Linear Models as a Baseline for Time Series Imputation. In XXX Simposio Brasileiro de Banco de Dados, Petropolis, RJ.
Sapankevych, N. and Sankar, R. (2009). Time series prediction using support vector machines: A survey. IEEE Computational Intelligence Magazine, 4(2):24–38.
Sfetsos, A. and Coonick, A. (2000). Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques. Solar Energy, 68(2):169–178.
Stewart, W. and Roberts, T. (2012). Food security and the role of fertilizer in supporting it. In Procedia Engineering, volume 46, pages 76–82.
Styhr Petersen, H. (1977). Forecasting Danish nitrogen fertilizer consumption. Industrial Marketing Management, 6(3):211–222.
Tenkorang, F. and Lowenberg-Deboer, J. (2009). Forecasting long-term global fertilizer demand. Nutrient Cycling in Agroecosystems, 83(3):233–247.
Thornton, C., Hutter, F., Hoos, H., and Leyton-Brown, K. (2013). Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, volume Part F128815, pages 847–855. UN (2019). United nations. Technical report, https://www.un.org/en/
Verma, T. and Pearl, J. (1991). Equivalence and synthesis of causal models. UCLA, Computer Science Department.
Zhang, G. and Berardi, V. (2001). Time series forecasting with neural network ensembles: An application for exchange rate prediction. Journal of the Operational Research Society, 52(6):652–664.
Zhang, W. and Zhang, X. (2007). A forecast analysis on fertilizers consumption worldwide.
Environmental Monitoring and Assessment, 133(1-3):427–434.
Zhao, Y., Ye, L., Li, Z., Song, X., Lang, Y., and Su, J. (2016). A novel bidirectional mechanism based on time series model for wind power forecasting. Applied Energy, 177:793–803.
Coulibaly, P., Anctil, F., and Bobee, B. (2001). Multivariate reservoir inflow forecasting using temporal neural networks. Journal of Hydrologic Engineering, 6(5):367–376.
Deadman, D. and Ghatak, S. (1979). Forecasting fertilizer consumption and production: Longand short-run models. World Development, 7(11-12):1063–1072.
Dobermann, A. and Cassman, K. (2005). Cereal area and nitrogen use efficiency are drivers of future nitrogen fertilizer consumption. Science in China. Series C, Life sciences / Chinese Academy of Sciences, 48 Spec No:745–758.
FAO (2019). Food and agriculture organization of the united nations. Technical report, http://www.fao.org.
Gilland, B. (1993). Cereals, nitrogen and population: an assessment of the global trends. Endeavour, 17(2):84–88.
Han, J., Kamber, M., and Pei, J. (2011). Data Mining: Concepts and Techniques. Morgan Kaufmann, Haryana, India; Burlington, MA, 3 edition.
Howarth, R., Boyer, E., Pabich, W., and Galloway, J. (2002). Nitrogen use in the United States from 1961-2000 and potential future trends. Ambio, 31(2):88–96.
Hyndman, R. and Khandakar, Y. (2008). Automatic time series forecasting: The forecast package for R. Journal of Statistical Software, 27(3):1–22.
Hyndman, R. J. and Koehler, A. B. (2006). Another look at measures of forecast accuracy. International journal of forecasting, 22(4):679–688.
Ogasawara, E., De Oliveira, D., Paschoal Jr., F., Castaneda, R., Amorim, M., Mauro, R., Soares, J., Quadros, J., and Bezerra, E. (2013). A forecasting method for fertilizers consumption in Brazil. International Journal of Agricultural and Environmental Information Systems, 4(2):23–36.
Ogasawara, E., Martinez, L., De Oliveira, D., Zimbrao, G., Pappa, G., and Mattoso, M. (2010). ˜Adaptive Normalization: A novel data normalization approach for non-stationary time series. In Proceedings of the International Joint Conference on Neural Networks.
Palmer, D., O’Boyle, N., Glen, R., and Mitchell, J. (2007). Random forest models to predict aqueous solubility. Journal of Chemical Information and Modeling, 47(1):150–158.
Pires, M., Da Cunha, D., De Matos Carlos, S., and Costa, M. (2015). Nitrogen-use efficiency, nitrous oxide emissions, and cereal production in Brazil: Current trends and forecasts. PLoS ONE, 10(8).
Salles, R., Belloze, K., Porto, F., Gonzalez, P., and Ogasawara, E. (2019). Nonstationary time series transformation methods: An experimental review. Knowledge-Based Systems, 164:274–291.
Salles, R., Bezerra, E., Soares, J., and Ogasawara, E. (2015). Evaluating Linear Models as a Baseline for Time Series Imputation. In XXX Simposio Brasileiro de Banco de Dados, Petropolis, RJ.
Sapankevych, N. and Sankar, R. (2009). Time series prediction using support vector machines: A survey. IEEE Computational Intelligence Magazine, 4(2):24–38.
Sfetsos, A. and Coonick, A. (2000). Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques. Solar Energy, 68(2):169–178.
Stewart, W. and Roberts, T. (2012). Food security and the role of fertilizer in supporting it. In Procedia Engineering, volume 46, pages 76–82.
Styhr Petersen, H. (1977). Forecasting Danish nitrogen fertilizer consumption. Industrial Marketing Management, 6(3):211–222.
Tenkorang, F. and Lowenberg-Deboer, J. (2009). Forecasting long-term global fertilizer demand. Nutrient Cycling in Agroecosystems, 83(3):233–247.
Thornton, C., Hutter, F., Hoos, H., and Leyton-Brown, K. (2013). Auto-WEKA: Combined selection and hyperparameter optimization of classification algorithms. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, volume Part F128815, pages 847–855. UN (2019). United nations. Technical report, https://www.un.org/en/
Verma, T. and Pearl, J. (1991). Equivalence and synthesis of causal models. UCLA, Computer Science Department.
Zhang, G. and Berardi, V. (2001). Time series forecasting with neural network ensembles: An application for exchange rate prediction. Journal of the Operational Research Society, 52(6):652–664.
Zhang, W. and Zhang, X. (2007). A forecast analysis on fertilizers consumption worldwide.
Environmental Monitoring and Assessment, 133(1-3):427–434.
Zhao, Y., Ye, L., Li, Z., Song, X., Lang, Y., and Su, J. (2016). A novel bidirectional mechanism based on time series model for wind power forecasting. Applied Energy, 177:793–803.
Publicado
30/06/2020
Como Citar
ANDRADE, Adalberto; SALLES, Rebecca; CARVALHO, Flavio; DA SILVA, Eduardo Bezerra; SOARES, Jorge; SOUZA, Cristina; GONZALEZ, Pedro Henrique; OGASAWARA, Eduardo.
Uso de ciência de dados para predição do consumo de fertilizantes no Brasil. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 14. , 2020, Cuiabá.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 9-16.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2020.11176.
