EMD-KNN Model for Annual Average Rainfall Forecasting
Publication: Journal of Hydrologic Engineering
Volume 18, Issue 11
Abstract
The prediction of rainfall is the premise of responding to water resources management and flood defense. The hydrological system is complex, and rainfall time series as a member of it has characteristics of being nonlinear and nonstationary, so accurate rainfall prediction still a much difficult job at present. The paper proposes a conjunction model named EMD-KNN for forecasting annual average rainfall. The model is improved by combining two methods, empirical mode decomposition (EMD) and the K-nearest neighbor bootstrap regressive model (K-NN). It is applied to case studies of forecasting annual average rainfall for Nanjing city and the Dahuofang reservoir basin. Where Nanjing city is water-rich area in East China, Dahuofang reservoir basin is water-deficient area in Northeast China. Three performance evaluation measures results revealed that the EMD-KNN model reduces the prediction mean absolute error (MAE), mean relative error (MRA), and root-mean square error (RMSE) with respect to the single K-NN model by almost 50% each, so the suggested model can effectively improve the forecast accuracy of single K-NN in forecasting the annual average rainfall.
Get full access to this article
View all available purchase options and get full access to this article.
References
Ajay, K., and Sajjad, A. (2009). “Using oceanic-atmospheric oscillation for long lead time streamflow forecasting.” Water Resour. Res., 45(3), W03413.
Bordignon, S., and Lisi, F. (2000). “Nonlinear analysis and prediction of river flow time series.” Environmetrics, 11(4), 463–477.
Chen, S. M., and Hsu, C. C. (2004). “A new method to forecast enrollments using fuzzy time series.” Int. J. App. Sci. Eng., 2(3), 234–244.
Cover, T. M., and Hart, P. E. (1967). “Nearest neighbor pattern classification.” IEEE Trans. Inform. Theory, 13(1), 21–27.
Deng, Y. J., Wang, W., Qian, C. C., Wang, Z., and Dai, D. J. (2001). “EMD method and the way to eliminate the influence of boundary effect of Hilbert Transform.” Chinese Sci. Bull., 46(3), 257–263.
Farmer, D. J., and Sidorowich, J. J. (1987). “Predicting chaotic time series.” Phys. Rev. Lett., 59(8), 845–848.
Feng, P., Ding, Z. H., and Han, R. G. (2008). “Time-scale analysis on annual runoff time series of the Taohe River based on EMD.” J. Arid Land Resour. Environ.,, 22(12), 73–76.
He, J. X., Yu, Z. H., and Yang, X. Q. (2005). “Temporal characteristics of Pacific decadal oscillation (PDO) and ENSO and their relationship analyzed with method of empirical mode decomposition(EMD).” Acta Meteorol. Sin., 19(1), 83–92.
Herrera, L. J., Pomares, H., Rojas, I., Guillén, A., Prieto, A., and Valenzuela, O. (2007). “Recursive prediction for long term time series forecasting using advanced models.” Neurocomputing, 70(16–18), 2870–2880.
Huang, N. E., et al. (1998). “The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis.” Proc. Roy. Soc. A, 454(1971), 903–995.
Huang, N. E., et al. (2003). “A confidence limit for the empirical mode decomposition and Hilbert spectral analysis.” Proc. Roy. Soc. A, 459(2037), 2317–2345.
Huang, H., and Pan, J. (2006). “Speech pitch determination based on Hilbert-Huang transform.” Signal Process., 86(4), 792–803.
Karlsson, M. S. (1985). “Nearest neighbor regression estimators in rainfall-runoff forecasting.” Ph.D. thesis, Univ. of Arizona, Tucson, AZ.
Karlsson, M., and Yakowitz, S. (1987). “Nearest-neighbor methods for nonparametric rainfall-runoff forecasting.” Water Resour. Res., 23(7), 1300–1308.
Kin, C. L., Ball, J. E., and Sharma, A. (2001). “An application of artificial neural networks for rainfall forecasting.” Math. Comput. Model., 33(6–7), 683–693.
Lai, R. J., and Huang, N. (2005). “Investigation of vertical and horizontal momentum transfer in the Gulf of Mexico using Empirical Mode Decomposition method.” J. Phys. Oceanogr., 35(8), 1383–1402.
Lall, U., and Sharma, A. (1996). “A nearest neighbor bootstrap for resampling hydrologic time series.” Water Resour. Res., 32(3), 679–694.
Mehrotra, R., and Sharma, A. (2006). “Conditional resampling of hydrologic time series using multiple predictor variables: A K-nearest neighbor approach.” Adv. Water Resour., 29(7), 987–999.
Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., and Veith, T. L. (2007). “Model evaluation guidelines for systematic quantification of accuracy in watershed simulations.” Trans. ASABE, 50(3), 885–900.
Nayak, P. C., Sudheer, K. P., Rangan, D. M., and Ramasastri, K. S. (2004). “Aneuro-fuzzy computing technique for modeling hydrological time series.” J. Hydrol., 291(1–2), 52–66.
Porporato, A., and Ridolfi, L. (2001). “Multivariate nonlinear prediction of river flows.” J. Hydrol., 248(1–4), 109–122.
Salisbury, J. I., and Wimbush, M. (2002). “Using modern time series analysis techniques to predict ENSO events from the SOI time series.” Nonlinear Proc. Geophys., 9(3/4), 341–345.
Shahab, A., Donald, H. B., and Mohammand, K. (2006). “Long-head probabilistic forecasting of streamflow using ocean-atmospheric and hydrological predictors.” Water Resour. Res., 42(3), W03431.
Shamseldin, A. Y., and O’Connor, K. M. (1996). “A nearest neighbor liner perturbation model for river flow forecasting.” J. Hydrol., 197(1–4), 203–229.
Sugihara, G., and May, R. M. (1990). “Nonlinear forecasting as a way of distinguishing chaos from measurement error in time series.” Nature, 344(6268), 734–741.
Van Liew, M. W., Arnold, J. G., and Garbrecht, J. D. (2003). “Hydrologic simulation on agricultural watersheds: choosing between two models.” Trans. ASAE, 46(6), 1539–1551.
Wang, Z. Z., Wang, Y. T., and Hu, S. Y., (2007). “Identify and analyze water resources evolution modes of the drainage basin.” J. Hydraul. Eng., 5(S), 387–392.
Xie, L. A., Pietrafesa, L. J., and Wu, K. J. (2002). “Inter-annual and decadal variability of land- falling tropical cyclone in southeast coastal states of the United States.” Adv. Atmos. Sci., 19(4), 677–686.
Yakowitz, S., and Karlsson, M. (1987). “Nearest neighbor methods for time series with application to rainfall-runoff prediction.” Stochastic Hydrology, J. B. MacNeil and G. J. Umphrey, eds., D. Reidel, Dordrecht, Netherlands, 149–160.
Yonaba, H., Anctil, F., and Fortin, V. (2010). “Comparing sigmoid transfer functions for neural network multistep ahead streamflow forecasting.” J. Hydrol. Eng., 15(4), 275–283.
Information & Authors
Information
Published In
Journal of Hydrologic Engineering
Volume 18 • Issue 11 • November 2013
Pages: 1450 - 1457
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Jun 9, 2010
Accepted: Jul 28, 2011
Published online: Jul 30, 2011
Discussion open until: Dec 30, 2011
Published in print: Nov 1, 2013
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.