Performance evaluation of signal decomposition methods in monthly precipitation estimation (case study: Telezang station)

Document Type : Research Paper

Authors

1 Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

2 Water Engineering Department, Shahid Chamran University of Ahvaz, Ahvaz, Iran.

10.22059/jwim.2025.396662.1239

Abstract

Precipitation forecasting is of great importance due to its impact on agriculture, natural disaster management, and water supply. Therefore, in this study, the monthly precipitation of the Telezang station from 1966 to 2020 was modeled using the Variable Mode Decomposition (VMD) and Complete Ensemble Empirical Mode Decomposition (CEEMD) methods. Input data were defined for the Support Vector Machine (SVM) model based on four scenarios. In the first scenario, monthly precipitation values with up to four lags were considered as model inputs. In the second scenario, in addition to the lagged precipitation data, a periodic term was added to the input patterns of the model. In the third and fourth scenarios, the monthly precipitation data were decomposed using CEEMD and VMD, respectively, and provided to the model. The findings of this research indicated that adding the periodic term slightly improved the model’s performance. Additionally, a comparison of the results from the data preprocessing methods using VMD and CEEMD showed that the VMD-SVM model outperformed the CEEMD-SVM model significantly, reducing the MAE index by an average of approximately 35.25 mm compared to the standalone model and 13.77 mm compared to the CEEMD-SVM model, while also achieving greater accuracy.

Keywords

Main Subjects

References

  1. (2020). Performance evaluation of support vector machine methods and adaptive neurofuzzy inference system in predicting monthly river flow (Case study of Nazlu and Sezar rivers). Iranian Soil and Water Research, 51(3), 673-686. (In Persian).
  2. Ali, M., Prasad, R., Xiang, Y., & Yaseen, Z. M. (2020). Complete ensemble empirical mode decomposition hybridized with random forest and kernel ridge regression model for monthly rainfall forecasts. Journal of Hydrology, 584, 124647.‏
  3. Alvanitopoulos, P. F., Andreadis, I., Georgoulas, N., Zervakis, M., & Nikolaidis, N. (2014). Solar radiation time-series prediction based on empirical mode decomposition and artificial neural networks. In Artificial Intelligence Applications and Innovations: 10th IFIP WG 12.5 International Conference, AIAI 2014, Rhodes, Greece, September 19-21, 2014. Proceedings 10 (pp. 447-455). Springer Berlin Heidelberg.‏
  4. Basak, D., Pal, S., & Patranabis, D. C. (2007). Support vector regression. Neural Information Processing-Letters and Reviews, 11(10), 203-224.‏
  5. Bauer, P., Thorpe, A., & Brunet, G. (2015). The quiet revolution of numerical weather prediction. Nature, 525(7567), 47-55.‏
  6. Bertsekas, D. P. (1976). Multiplier methods: A survey. Automatica, 12(2), 133-145.‏
  7. Bertsekas, D. P. (2014). Constrained optimization and Lagrange multiplier methods. Academic press.‏
  8. Dong, J., Wang, Z., Wu, J., Cui, X., & Pei, R. (2024). A novel runoff prediction model based on support vector machine and gate recurrent unit with secondary mode decomposition. Water Resources Management, 38(5), 1655-1674.‏
  9. Dong, S. (2024). Enhanced Prediction of Extreme Monthly Precipitation in Yinchuan City Using CEEMDAN-Decomposed LSTM and BP Neural Networks. In 2024 International Conference on Advances in Electrical Engineering and Computer Applications (AEECA) (pp. 491-496). IEEE.‏
  10. Dotse, S. Q., Larbi, I., Limantol, A. M., & De Silva, L. C. (2024). A review of the application of hybrid machine learning models to improve rainfall prediction. Modeling Earth Systems and Environment, 10(1), 19-44.‏
  11. Dragomiretskiy, K., & Zosso, D. (2013). Variational mode decomposition. IEEE transactions on signal processing, 62(3), 531-544.‏
  12. Du, J., Liu, Y., Yu, Y., & Yan, W. (2017). A prediction of precipitation data based on support vector machine and particle swarm optimization (PSO-SVM) algorithms. Algorithms, 10(2), 57.‏
  13. El-Shafie, A., Jaafer, O., & Seyed, A. (2011). Adaptive neuro-fuzzy inference system based model for rainfall forecasting in Klang River, Malaysia. International Journal of Physical Sciences, 6(12), 2875-2888.‏
  14. Essam, Y., Huang, Y. F., Ng, J. L., Birima, A. H., Ahmed, A. N., & El-Shafie, A. (2022). Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms. Scientific Reports, 12(1), 38-83.‏
  15. Fardi Rad & Hosseini. (2025). Evaluation of a hybrid model for estimating monthly runoff using support vector machine based on variable mode analysis with whale optimization algorithm (Mashhad study basin). Iranian Water Research, 12(1), 39-53.‏ (In Persian).
  16. Haddad, M. S. (2011). Capacity choice and water management in hydroelectricity systems. Energy Economics, 33(2), 168-177.‏
  17. Hartmann, H., Snow, J. A., Stein, S., Su, B., Zhai, J., Jiang, T., & Kundzewicz, Z. W. (2016). Predictors of precipitation for improved water resources management in the Tarim River basin: Creating a seasonal forecast model. Journal of Arid Environments, 125, 31-42.‏
  18. Hestenes, M. R. (1969). Multiplier and gradient methods. Journal of optimization theory and applications, 4(5), 303-320.‏
  19. Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., & Liu, H. H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceedings of the Royal Society of London. Series A: mathematical, physical and engineering sciences, 454(1971), 903-995.‏
  20. Imran, M., Jannat Mishu, N. E., Khaliq, H., & Shahzad, F. (2025). Assessing Machine Learning Models for Precipitation Prediction in the Upper Indus Basin: A Comparative Analysis. Environmental Science and Ecology, 6(1), 10108.‏
  21. Jiang, L., & Wu, J. (2013). Hybrid PSO and GA for neural network evolutionary in monthly rainfall forecasting. In Intelligent Information and Database Systems: 5th Asian Conference, ACIIDS 2013, Kuala Lumpur, Malaysia, March 18-20, 2013, Proceedings, Part I 5 (pp. 79-88). Springer Berlin Heidelberg.‏
  22. Kalteh, A. M. (2017). Enhanced monthly precipitation forecasting using artificial neural network and singular spectrum analysis conjunction models. INAE Letters, 2, 73-81.‏
  23. Lahmiri, S. (2016). Intraday stock price forecasting based on variational mode decomposition. Journal of Computational Science, 12, 23-27.‏
  24. Lahmiri, S., & Boukadoum, M. (2014, October). Biomedical image denoising using variational mode decomposition. In 2014 IEEE biomedical circuits and systems conference (BioCAS) proceedings (pp. 340-343). IEEE.‏
  25. Lahmiri, S., & Shmuel, A. (2017). Variational mode decomposition based approach for accurate classification of color fundus images with hemorrhages. Optics & Laser Technology, 96, 243-248.‏
  26. Lian, L. (2022). Runoff forecasting model based on CEEMD and combination model: a case study in the Manasi River, China. Water Supply, 22(4), 3921-3940.‏
  27. Liu, W., Cao, S. Y., & He, Y. (2015). Ground roll attenuation using variational mode decomposition. In 77th EAGE Conference and Exhibition 2015 (Vol. 2015, No. 1, pp. 1-5). European Association of Geoscientists & Engineers.‏
  28. Liu, W., Cao, S., & Chen, Y. (2016). Applications of variational mode decomposition in seismic time-frequency analysis. Geophysics, 81(5), V365-V378.‏
  29. Liu, W., Liu, Y., Li, S., & Chen, Y. (2023). A review of variational mode decomposition in seismic data analysis. Surveys in Geophysics, 44(2), 323-355.‏
  30. Mahmood, O. A., Sulaiman, S. O., & Al-Jumeily, D. (2024). Forecasting for Haditha reservoir inflow in the West of Iraq using Support Vector Machine (SVM). PloS one, 19(9), e0308266.‏
  31. Montaseri & Ghavidel. (2017). Comparison of the performance of artificial intelligence models in estimating river water quality parameters during periods of drought and flood. Water and Soil, 30(6), 1733-1747. (In Persian).
  32. Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I—A discussion of principles. Journal of Hydrology, 10(3), 282-290.‏
  33. Nocedal, J., & Wright, S. J. (Eds.). (1999). Numerical optimization. New York, NY: Springer New York.‏
  34. Parsaie, A., Ghasemlounia, R., Gharehbaghi, A., Haghiabi, A., Chadee, A. A., & Nou, M. R. G. (2024). Novel hybrid intelligence predictive model based on successive variational mode decomposition algorithm for monthly runoff series. Journal of Hydrology, 634, 131041.‏
  35. Pham, B. T., Le, L. M., Le, T. T., Bui, K. T. T., Le, V. M., Ly, H. B., & Prakash, I. (2020). Development of advanced artificial intelligence models for daily rainfall prediction. Atmospheric Research, 237, 104845.‏
  36. Putri, A. L. R. (2024). Performance Analysis of the Support Vector Machine Algorithm in Predicting Rain Potential in DKI Jakarta. Journal of Artificial Intelligence and Engineering Applications, 3(3), 671-676.‏
  37. Rockafellar, R. T. (1973). A dual approach to solving nonlinear programming problems by unconstrained optimization. Mathematical programming, 5(1), 354-373.‏
  38. Roodaki, S., & Azizian, A. (2020). Uncertainty analysis due to the application of different infiltration methods on the performance of HEC-HMS model using GLUE algorithm. Iran-Water Resources Research, 16(2), 50-66.‏ (In Persian).
  39. Samantaray, S., Das, S. S., Sahoo, A., & Satapathy, D. P. (2022). Monthly runoff prediction at Baitarani river basin by support vector machine based on Salp swarm algorithm. Ain Shams Engineering Journal, 13(5), 101732.‏
  40. Schultz, M. G., Betancourt, C., Gong, B., Kleinert, F., Langguth, M., Leufen, L. H., ... & Stadtler, S. (2021). Can deep learning beat numerical weather prediction?. Philosophical Transactions of the Royal Society, 379(2194), 20200097.‏
  41. Shen, Z. Y., & Ban, W. C. (2023). Machine learning model combined with CEEMDAN algorithm for monthly precipitation prediction. Earth Science Informatics, 16(2), 1821-1833.‏
  42. Tian, Z., & Chen, H. (2021). Multi-step short-term wind speed prediction based on integrated multi-model fusion. Applied Energy, 298, 117248.‏
  43. Torres, M. E., Colominas, M. A., Schlotthauer, G., & Flandrin, P. (2011). A complete ensemble empirical mode decomposition with adaptive noise. In 2011 IEEE international conference on acoustics, speech and signal processing (ICASSP) (pp. 4144-4147). IEEE.
  44. Tricha, A., & Moussaid, L. (2024). Evaluating machine learning models for precipitation prediction in Casablanca City. Indonesian Journal of Electrical Engineering and Computer Science, 35(2), 1325-1332.‏
  45. Trinh, T. A. (2018). The impact of climate change on agriculture: findings from households in Vietnam. Environmental and resource economics, 71(4), 897-921.‏
  46. Vapnik, V. (1998). The support vector method of function estimation. In Nonlinear modeling: Advanced black-box techniques (pp. 55-85). Boston, MA: springer us.‏
  47. Vapnik, V. (1995). The nature of statistical learning theory Springer. New York. 10: 978-1.‏
  48. Xu, H., Guo, Z., Cao, Y., Cheng, X., Zhang, Q., & Chen, D. (2024). Research on short-term precipitation forecasting method based on CEEMDAN-GRU algorithm. Scientific Reports, 14(1), 31885.‏
  49. Xue, Y. J., Cao, J. X., Wang, X. J., Li, Y. X., & Du, J. (2019). Recent developments in local wave decomposition methods for understanding seismic data: Application to seismic interpretation. Surveys in Geophysics, 40, 1185-1210.‏
  50. Yao, C., Yang, Z., Bai, H., Wu, Y., Gong, Z., & Feng, G. (2024). Ensemble prediction of winter precipitation in China based on Support Vector Machine Method. Chinese Journal of Geophysics, 67(10), 3670-3685.‏
  51. Yeh, J. R., Shieh, J. S., & Huang, N. E. (2010). Complementary ensemble empirical mode decomposition: A novel noise enhanced data analysis method. Advances in adaptive data analysis, 2(02), 135-156.‏
  52. Zakaria, Z. A., & Shabri, A. (2012). Streamflow forecasting at ungaged sites using support vector machines. Applied Mathematical Sciences, 6(60), 3003-3014.‏
  53. Zhang, S., Zhu, K., & Wang, C. (2025). A Novel Monthly Runoff Prediction Model Based on KVMD and KTCN-LSTM-SA. Water, 17(3), 460.‏
  54. Zhang, X., Shi, J., Chen, H., Xiao, Y., & Zhang, M. (2023). Precipitation prediction based on CEEMDAN–VMD–BILSTM combined quadratic decomposition model. Water Supply, 23(9), 3597-3613.‏
  55. Zhang, X., Zhang, Q., Zhang, G., Nie, Z., Gui, Z., & Que, H. (2018). A novel hybrid data-driven model for daily land surface temperature forecasting using long short-term memory neural network based on ensemble empirical mode decomposition. International journal of environmental research and public health, 15(5), 1032.‏
  56. Zhang, Y. A., Yan, B., & Aasma, M. (2020). A novel deep learning framework: Prediction and analysis of financial time series using CEEMD and LSTM. Expert systems with applications, 159, 113609.
Water and Irrigation Management
Volume 15, Issue 3
December 2025
Pages 525-542

Files

Share

How to cite

Statistics