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Drought Forecasting Using Gaussian Process Regression (GPR) and Empirical Wavelet Transform (EWT)-GPR in Gua Musang

  • Muhammad Akram Shaari
  • Ruhaidah SamsudinEmail author
  • Ani Shabri Ilman
  • Abdulsamad E. Yahya
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1073)

Abstract

Drought forecasting is important in preparing for drought and its mitigation plan. This study focuses on the investigating the performance of Gaussian Process Regression (GPR) and Empirical Wavelet Transform-Gaussian Process Regression (EWT-GPR) in forecasting drought using Standard Precipitation Index (SPI). Daily rainfall data from Gua Musang, Kelantan from 1975 to 2008 was used in this study. SPI data of 3, 6, 9, and 12 months were then calculated using the rainfall data. EWT was employed to decompose the time series into several finite modes. EWT was used to create Intrinsic Mode Functions (IMF) which are used to create ARIMA models. The objective of this study is to compare the effectiveness of the proposed method against GPR model in the accuracy of forecasting drought in Arau, Malaysia. It was found that the proposed model performed better compared to GPR model in all SPI studied.

Keywords

Forecasting Drought Gaussian Process Regression (GPR) Empirical Wavelet Transform (EWT) 

Notes

Acknowledgement

The authors would like to express their deepest gratitude to Research Management Center (RMC), Universiti Teknologi Malaysia (UTM), Ministry of Higher Education (MOHE) and Ministry of Science, Technology and Innovation (MOSTI) for their financial support under Grant Vot 4F875.

References

  1. 1.
    Belayneh, A., Adamowski, J., Khalil, B., Ozga-Zielinski, B.: Long-term SPI drought forecasting in the Awash River Basin in Ethiopia using wavelet neural network and wavelet support vector regression models. J. Hydrol. 508, 418–429 (2014).  https://doi.org/10.1016/j.jhydrol.2013.10.052CrossRefGoogle Scholar
  2. 2.
    Sun, A.Y., Wang, D., Xu, X.: Monthly streamflow forecasting using Gaussian Process Regression. J. Hydrol. 511, 72–81 (2014).  https://doi.org/10.1016/j.jhydrol.2014.01.023CrossRefGoogle Scholar
  3. 3.
    Hu, J., Wang, J.: Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression. Energy 93, 1456–1466 (2015).  https://doi.org/10.1016/j.energy.2015.10.041CrossRefGoogle Scholar
  4. 4.
    Rohani, A., Taki, M., Abdollahpour, M.: A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I). Renew. Energy 115, 411–422 (2018).  https://doi.org/10.1016/j.renene.2017.08.061CrossRefGoogle Scholar
  5. 5.
    Rasmussen, C.E.: Gaussian processes for machine learning. Presented at the (2006)Google Scholar
  6. 6.
    Guang, Y.Y.U., Hu, Z.Y.U., Liu, X.S.I.: A novel strategy for wind speed prediction in wind farm. TELKOMNIKA Indones. J. Electr. Eng. 11, 7007–7013 (2013)Google Scholar
  7. 7.
    Guo, Z., Zhao, W., Lu, H., Wang, J.: Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model. Renew. Energy 37, 241–249 (2012).  https://doi.org/10.1016/j.renene.2011.06.023CrossRefGoogle Scholar
  8. 8.
    Liu, Y.P., Wang, Y., Wang, Z.: RBF prediction model based on EMD for forecasting GPS precipitable water vapor and annual precipitation. Adv. Mater. Res. 765–767, 2830–2834 (2013). https://doi.org/10.4028/www.scientific.net/AMR.765-767.2830CrossRefGoogle Scholar
  9. 9.
    Shabri, A.: A modified EMD-ARIMA based on clustering analysis for fishery landing forecasting 10, 1719–1729 (2016).  https://doi.org/10.12988/ams.2016.6389
  10. 10.
    Gilles, J.: Empirical wavelet transform. IEEE Trans. Signal Process. 61, 3999–4010 (2013).  https://doi.org/10.1109/TSP.2013.2265222MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Djerbouai, S., Souag-Gamane, D.: Drought forecasting using neural networks, wavelet neural networks, and stochastic models: case of the Algerois Basin in North Algeria. Water Resour. Manag. 30, 2445–2464 (2016)CrossRefGoogle Scholar
  12. 12.
    Pandhiani, S.M., Shabri, A.B.: Time series forecasting using wavelet-least squares support vector machines and wavelet regression models for monthly stream flow data. Open J. Stat. 3, 183 (2013)CrossRefGoogle Scholar
  13. 13.
    Hu, J., Wang, J., Ma, K.: A hybrid technique for short-term wind speed prediction. Energy 81, 563–574 (2015).  https://doi.org/10.1016/j.energy.2014.12.074CrossRefGoogle Scholar
  14. 14.
    Shaaban, A.J., Low, K.S.: Droughts in Malaysia: a look at its characteristics, impacts, related policies and management strategies. Presented at the Water and Drainage 2003 Conference (2003)Google Scholar
  15. 15.
    Gilles, J., Heal, K.: A parameterless scale-space approach to find meaningful modes in histograms—application to image and spectrum segmentation. Int. J. Wavelets Multiresolution Inf. Process. 12, 1450044 (2014).  https://doi.org/10.1142/S0219691314500441MathSciNetCrossRefzbMATHGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Muhammad Akram Shaari
    • 1
  • Ruhaidah Samsudin
    • 1
    Email author
  • Ani Shabri Ilman
    • 2
  • Abdulsamad E. Yahya
    • 3
  1. 1.School of Computing, Faculty of EngineeringUniversiti Teknologi MalaysiaSkudaiMalaysia
  2. 2.Department of Mathematics, Faculty of ScienceUniversiti Teknologi MalaysiaSkudaiMalaysia
  3. 3.Northern Border UniversityArarKingdom of Saudi Arabia

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