Integration of SVR and GPR Algorithms with Wavelet in Modeling Monthly Drought Forecasting

Document Type : Research Paper

Authors

• Jahanbakhsh Mohammadi, ¹
• Alireza Vafaeinejad ²
• Saeed Behzadi ³
• Hossein Aghamohammadi ⁴
• Amirhooman Hemmasi ⁵

¹ Ph.D. Student, Department of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran.

² Associate Professor, Faculty of Civil, Water and Environmental Engineering, Shahid Beheshti University, Tehran, Iran.

³ Assistant Professor, Faculty of Civil Engineering Shahid Rajaee Teacher Training University, Tehran, Iran.

⁴ Assistant Professor, Department of Remote Sensing and GIS, Faculty of Natural Resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran

⁵ Professor, Natural Resources Engineering, Faculty of Natural Resources and Environment, Tehran science and Research Branch, Islamic Azad University, Tehran, Iran.

10.30495/wej.2023.29419.2344

Abstract

Abstract
Introduction: Drought is one of the natural hazards that have random and nonlinear behavior due to its various climatic parameters. SPI index is the most common index extracted from rainfall that has been used in modeling drought by various researchers.
Methods: The use of computational intelligence methods to model drought in recent years has been much considered by researchers in the field of water resources. In this research, SVR and GPR algorithms individually and also the combination of these algorithms with wavelet algorithms have been modeled and predicted by SPI index, and the purpose was to evaluate the improvement of computational intelligence algorithms in combination with wavelet. In this research, the time series data of 10 synoptic stations in Iran in the period 1961 to 2017 have been used on a monthly basis for modeling the drought as the input of the studied algorithms.
Findings: The results of this study showed that the use of the wavelet method in combination with SVR and GPR computational intelligence algorithms improved the results in all time scales. Also, the modeling improvement is due to the use of wavelet in combination with the SVR model with an average RMSE difference of -0.1540 and R² difference of 0.1491 and the GPR model with an average RMSE difference of -0.1554 and R² difference of 0.1530 Compared to the single SVR and GPR models showed that the GPR model in general (all time scales and all stations) had a better improvement in the hybrid model than the single model.

Keywords

• Drought
• Wavelet
• SPI
• SVR
• GPR

20.1001.1.20086377.1402.16.56.7.3

Main Subjects

• subject

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References

1.       Abeysingha NS, Rajapaksha URLN. 2020.  SPI-Based Spatiotemporal Drought over Sri Lanka. Advances in Meteorology.

2.       Azimi S, Azhdary Moghaddam M. 2020. Modeling Short Term Rainfall Forecast Using Neural Networks, and Gaussian Process Classification Based on the SPI Drought Index. Water Resources Management. 34(4):1369–405.

3.       Bhunia P, Das P, Maiti R. 2020. Meteorological drought study through SPI in three drought prone districts of West Bengal, India. Earth Systems and Environment. 4(1):43–55.

4.       Raghavendra S, Deka PC. 2014. Support vector machine applications in the field of hydrology: A review. Applied soft computing. 19:372–86.

5.       Djerbouai S, Souag-Gamane D. 2016. Drought forecasting using neural networks, wavelet neural networks, and stochastic models: case of the Algerois Basin in North Algeria. Water Resources Management. 30(7):2445–64.

6.       Drisya J, Kumar DS, Roshni T. 2021. Hydrological drought assessment through streamflow forecasting using wavelet enabled artificial neural networks. Environment, Development and Sustainability. 23(3):3653–72.

7.       Ghasemi P, Karbasi M, Nouri AZ, Tabrizi MS, Azamathulla HM. 2021. Application of Gaussian process regression to forecast multi-step ahead SPEI drought index. Alexandria Engineering Journal. 60(6):5375–92.

8.       Tarebari H, Javid AH, Mirbagheri SA, Fahmi H. 2018. Multi-objective surface water resource management considering conflict resolution and utility function optimization. Water Resources Management. 32:4487–509.

9.       Hong X, Ding Y, Ren L, Chen L, Huang B. 2018. A weighted heteroscedastic Gaussian Process Modelling via particle swarm optimization. Chemometrics and Intelligent Laboratory Systems. 172:129–38.

10.   Khan MMH, Muhammad NS, El-Shafie A. 2020. Wavelet based hybrid ANN-ARIMA models for meteorological drought forecasting. Journal of Hydrology. 590:125380.

11.   KIŞI Ö. 2006. Generalized regression neural networks for evapotranspiration modelling. Hydrological Sciences Journal. 51(6):1092–105.

12.   Komasi M, Sharghi S. 2020. Drought Forecasting Using Wavelet-Support Vector Machine and Standardized Precipitation Index (Case Study: Urmia Lake-Iran). Journal of Environmental Science and Technology. 22(7):83–101.

13.   Lazri M, Ameur S, Brucker JM, Lahdir M, Sehad M. 2015. Analysis of drought areas in northern Algeria using Markov chains. Journal of Earth System Science. 124:61–70.

14.   Li G, Liu Z, Li J, Fang Y, Liu T, Mei Y, et al. 2018. Application of general regression neural network to model a novel integrated fluidized bed gasifier. International Journal of Hydrogen Energy. 43(11):5512–21.

15.   Li L, She D, Zheng H, Lin P, Yang Z-L. 2020. Elucidating diverse drought characteristics from two meteorological drought indices (SPI and SPEI) in China Journal of Hydrometeorology. 21(7):1513–30.

16.   McKee TB, Doesken NJ, Kleist J. 1993. The relationship of drought frequency and duration to time scales. In: Proceedings of the 8th Conference on Applied Climatology. p. 179–83.

17.   Mehdizadeh S, Ahmadi F, Mehr AD, Safari MJS. 2020. Drought modeling using classic time series and hybrid wavelet-gene expression programming models. Journal of Hydrology. 587:125017.

18.   Neal RM. 1997. Monte Carlo implementation of Gaussian process models for Bayesian regression and classification. arXiv preprint physics /9701026.

19.   Pal M, Deswal S. 2010. Modelling pile capacity using Gaussian process regression. Computers and Geotechnics. 37(7–8):942–7.

20.   Paulo AA, Pereira LS. 2007. Prediction of SPI drought class transitions using Markov chains. Water resources management. 21:1813–27.

21.   Pei Z, Fang S, Wang L, Yang W. 2020. Comparative analysis of drought indicated by the SPI and SPEI at various timescales in inner Mongolia, China. Water. 12(7):1925.

22.   Tay FEH, Cao L. 2001. Application of support vector machines in financial time series forecasting. Omega. 29(4):309–17.

23.   Taylan ED, Terzi Ö, Baykal T. 2021. Hybrid wavelet–artificial intelligence models in meteorological drought estimation. Journal of Earth System Science. 130:1–13.

24.   Won J, Choi J, Lee O, Kim S. 2020. Copula-based Joint Drought Index using SPI and EDDI and its application to climate change. Science of the Total Environment. 744:140701.

25.   Xu D, Zhang Q, Ding Y, Huang H. 2020. Application of a hybrid ARIMA–SVR model based on the SPI for the forecast of drought—a case study in Henan Province, China. Journal of Applied Meteorology and Climatology. 59(7):1239–59.

26.   Yang D, Zhang X, Pan R, Wang Y, Chen Z. 2018. A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve. Journal of Power Sources. 384:387–95.

27.     Zhang C, Wei H, Zhao X, Liu T, Zhang K. 2016. A Gaussian process regression based hybrid approach for short-term wind speed prediction. Energy conversion and management. 126:1084–92.