Forecasting of rainfall using different input selection methods on climate signals for neural network inputs

Document Type : Research Paper

Authors

1 Water Resources Division, Department of Civil Engineering, KN Toosi University of Technology

2 Department of Civil Engineering, KN University of Technology, Tehran, Iran

Abstract

Long-term prediction of precipitation in planning and managing water resources, especially in arid and semi-arid countries such as Iran, has a great importance. In this paper, a method for predicting long-term precipitation using weather signals and artificial neural networks is presented. For this purpose, climatic data (large-scale signals) and meteorological data (local precipitation and temperature) with 3 to 12 months lead-times are used as inputs to predict precipitation for 3, 6, 9 and 12 months periods in 6 selected stations across Iran. A genetic algorithm (GA) and self-organized neural network (SOM) along with the application of winGamma software were comparatively used as input selection methods to choose the appropriate input variables. Examining the results, out of 96 predictions performed at all stations, in 43 cases, GA, in 28 cases, winGamma, and in 25 cases SOM have the best results compared to the other two methods. According to this, as a generalized assumption, it can be said that at least for the selected stations in this paper, the GA method is more reliable than the other two methods, and can be used to make predictions for future applications as a reliable input selection method. Moreover, among different climatic signals, Pacific Decadal Oscillation (PDO), Trans-Niño Index (TNI) and Eastern Tropical Pacific SST (NINO3) are the most repetitive indices for the most accurate forecast of each station.

Keywords

References

  1. Redmond K.T. and Koch, R.W., (1991). Surface climate and streamflow variability in the western United States and their relationship to Large-Scale circulation Indices. Water Resources Research, 27, 2381-2399.
  2. Harzallah, A., and Sadourny, R. (1997). Observed lead-lag relationship between Indian summer monsoon and some meteorological variables, Clim. Dn. 13, 635- 48.
  3. Chiew F.H.S., Piechota T.C., Dracup J.A., McMahon T.A. (1998). El Nino/Southern Oscillation and Austrailian drought: link and potential for forecasting. Journal of Hydrology, 3, 138-149.
  4. Sharma. A., Luck, K. C., Cordery. I., Lall. U. (2000). Seasonal to Interannual Rainfall Probabilistic Forecast for Improved Water Supply Management: Part 2- Predictor Identification of Quarterly Rainfall Using Ocean-Atmosphere Information., Journal of Hydrology, Vol. 239, 2000, pp. 240-248.
  5. Sperber K. R., Slingo J. M., Annamalai H., (2000). Predictability and the relationship between subseasonal and interannual variability during the Asian summer monsoon., Q. J. R. Meteorol. Soc. 126, 2545-2574.
  6. Nazemosadat, M. J., & Cordery, I. (2000). On the relationships between ENSO and autumn rainfall in Iran. International Journal of Climatology, 20(1), 47-61.
  7. Curtis, S., Piechota T. C., Dracup J. A., (2001). Evaluation of tropical and extratropical precipitation anomalies during the 1997-1999 ENSO cycle., Int. J. of Climatology, Vol. 21, pp. 961-971.
  8. Cullen, H. M., Kaplan A., Arkin Ph. A. and and deMenocal P. B., (2002). Impact of the North Atlantic Oscillation on Middle Eastern Climate and Streamflow., Climatic Change, 55, 315.338.
  9. Pineda LE, Willems P. Rainfall extremes, weather and climate drivers in complex terrain: A data-driven approach based on signal enhancement methods and EV modeling. Journal of hydrology. 2018 Aug 1; 563:283-302.
  10. Kisembe J, Favre A, Dosio A, Lennard C, Sabiiti G, Nimusiima A. Evaluation of rainfall simulations over Uganda in CORDEX regional climate models. Theoretical and Applied Climatology. 2018 Oct: 1-8.
  11. Armal S, Devineni N, Khanbilvardi R. Trends in extreme rainfall frequency in the contiguous United States: Attribution to climate change and climate variability modes. Journal of Climate. 2018 Jan; 31(1):369-85.
  12. Jeong, D. I., Kim, Y.-O., Kim, N. I., and Ko, I. H., (2004), An overview of ensemble streamflow prediction studies in Korea, Proceeding of 2nd Asia Pacific.
  13. Kumar, D. N., Reddy, M. J., and Maity, R. (2007). “Regional rainfall forecasting using large scale climate teleconnections and artificial intelligence techniques.” Journal of Intelligent Systems, 16(4), 307-322.
  14. Salas, J. D., Fu, C., and Rajagopalan, B. (2011). “Long-range forecasting of Colorado streamflows based on hydrologic, atmospheric, and oceanic data.” J. Hydrol. Eng., 16(6), 508-520.
  15. Zahraie, B., Karannotiz M., and Eghdaini, S. (2004), "seasonal Precipitation Forecasting Using Large Scale Climate Signals: Application to the Karoon River Basin in Iran". Proceedings of the 6th International Conference on Hydroinformatics - Liong, Phoon & Bavic.
  16. Kiernan, L., Kambhampati, C., & Mitchell, R. J. (1995). Using self-organizing feature maps for feature selection in supervised neural networks.
  17. Nourani, V., Baghanam, A. H., Adamowski, J., & Gebremichael, M. (2013). Using self-organizing maps and wavelet transforms for space–time pre-processing of satellite precipitation and runoff data in neural network-based rainfall–runoff modeling. Journal of hydrology, 476, 228-243.
  18. Sinha P, Mann ME, Fuentes JD, Mejia A, Ning L, Sun W, He T, Obeysekera J. Downscaled rainfall projections in south Florida using self-organizing maps. Science of the Total Environment. 2018 Sep 1; 635:1110-23.
  19. Parchure AS, Gedam SK. Precipitation Regionalization Using Self-Organizing Maps for Mumbai City, India. Journal of Water Resource and Protection. 2018 Sep 4;10(09):939.
  20. Lee, J., Kim, J., Lee, J. H., Cho, I. H., Lee, J. W., Park, K. H., & Park, J. (2012, November). Feature selection for heavy rain prediction using genetic algorithms. In Soft Computing and Intelligent Systems (SCIS) and 13th International Symposium on Advanced Intelligent Systems (ISIS), 2012 Joint 6th International Conference on (pp. 830-833). IEEE.
  21. Venkadesh, S., Hoogenboom, G., Potter, W., & McClendon, R. (2013). A genetic algorithm to refine input data selection for air temperature prediction using artificial neural networks. Applied Soft Computing, 13(5), 2253-2260.
  22. Dariane, A.B., Azimi Sh., (2016). Forecasting streamflow by combination of genetic input selection algorithm and wavelet transform using ANFIS model. Hydrological Sciences Journal, vol. 61(3), pp. 585-600.
  23. Remesan, R., Shamim, M. A., & Han, D. (2008). Model data selection using gamma test for daily solar radiation estimation. Hydrological processes, 22(21), 4301-4309.
  24. Rauf, A., Ahmed, Sh., Ghumman, A.R., Ahmad, I., Khan, K.A., Ahsan, M. (2016). Data Driven Modelling for Real-time Flood Forecasting. 2nd International Multi-Disciplinary Conference Gujrat, Pakistan.
  25. Singh A, Malik A, Kumar A, Kisi O. Rainfall-runoff modeling in hilly watershed using heuristic approaches with gamma test. Arabian Journal of Geosciences. 2018 Jun 1; 11(11):261.
  26. https://climate.nasa.gov/
  27. Grantz, K., Rajagopalan, B., Clark, M., and Zagona, E. (2005). “A technique for incorporating large‐scale climate information in basin‐scale ensemble streamflow forecasts.” Water Resour. Res., 41(10).
  28. Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., and Woollen, J. (1996). “The NCEP/NCAR 40-year reanalysis project.” Bulletin of the American meteorological Society, 77(3), 437-471.
  29. Coulibaly, P., Anctil, F., Rasmussen, P., & Bobée, B. (2000). A recurrent neural networks approach using indices of low‐frequency climatic variability to forecast regional annual runoff. Hydrological Processes, 14(15), 2755-2777.
  30. Holland, J. H. (1975). Adaptation in Natural and Artificial Systems. Ann Arbor: University of Michigan Press.
  31. Http://users.cs.cf.ac.uk/O.F.Rana/Antonia.J.Jones/GammaArchive/Gamma%20Software/winGamma/winGamma.htm
  32. Kohonen, T. (1982). “Self-organized formation of topologically correct feature maps.” Bio Cybern, 43(1), 59-69.

Files

Share

How to cite

Statistics