Application of Hybrid Least Square Support Vector Machine-Whale Optimization Algorithm (LSSVM-WOA) for Downscaling and Prediction of Precipitation under Climate Change (Case Study: Karun3 Basin)

Document Type : Original Article

Authors

1 Graduated of Water Resources Engineering and Management, Faculty of Civil Engineering, Semnan University, Semnan, Iran.

2 Assistant Professor, Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran

3 Department of Water Engineering and Hydraulic Structures, Faculty of Civil Engineering, Semnan University, Semnan, Iran

4 Assistant Professor, Department of Civil Engineering, Semnan University, Semnan, Iran

10.22125/iwe.2021.128204

Abstract

In the present study, precipitation in six stations of Karun3 basin is downscaled by using the hybrid of least squares support vector machine and whale optimization algorithm (LSSVM-WOA), K nearest neighbor (KNN), and artificial neural network (ANN). For downscaling precipitation, first, the days of year are classified into wet and dry days by using MARS and M5 algorithms. Then, the amount of precipitation for wet days is estimated by using each of LSSVM-WOA, KNN and ANN methods. Based on the findings, MARS algorithm is superior over M5 algorithm. Based on the mean precipitation in the six stations, ANN is a little bit better than LSSVM-WOA (0.5 percent more accurate). While, by regarding the mean of standard deviations, the Nash-Sutcliff for Ann is up to 5.04 percent more accurate than LSSVM-WOA. Eventually, the amount of precipitation is predicted based on the CanESM2 model under RCP2.6, RCP4.5 and RCP8.5 scenarios for 2020-2040 and 2070-2100 periods. Based on the results of applying LSSVM-WOA, the precipitation in each three scenarios is decreased compared to the base period. Maximum decrease of precipitation (18%) is calculated by RCP8.5 for 2070-2100 period. Minimum decrease of precipitation (1%) is related to RCP2.6 scenario for 2020-2040 future period. But, the precipitation variation amount that is predicted by ANN is between -43 and 72 percent. Therefore, the results of LSSVM-WOA are more reliable and less uncertain

Keywords

References

آبکار، ع، ج.، م. حبیب نژاد، ک. سلیمانی و ه. نقوی. بررسی میزان کارایی مدل SDSM در شبیه­سازی شاخص­های دمایی در مناطق خشک و نیمه­خشک. 1392. نشریه مهندسی آبیاری و آب. سال چهارم، شماره 14، ص 1-17.
Ahmadi, A., A. Moridi, E. K. Lafdani and G. Kianpisheh. 2014. Assessment of climate change impacts on rainfall using large scale climate variables and downscaling models–A case study. Journal of Earth System Science, 123(7): 1603-1618.‏
Al-Shammari, E. T., K. Mohammadi, A. Ab. Keivani, Sh. Hamid, S. Akib, S. Shamshirband and  D. Petković. 2016. Prediction of daily dewpoint temperature using a model combining the support vector machine with firefly algorithm. Journal of Irrigation and Drainage Engineering, 142(5): 04016013.
Ashofteh, P. S., O. Bozorg Haddad and MA. Mariño. 2012. Climate change impact on reservoir performance indexes in agricultural water supply. Journal of Irrigation and Drainage Engineering, 139(2): 85-97.
Beheshti, Z., M. Firouzi, SM. Shamsuddin, M. Zibarzani and Z. Yusop. 2016. A new rainfall forecasting model using the CAPSO algorithm and an artificial neural network,.Neural Computing and Applications, 27(8): 2551-2565.
Chen, S.T., P. S. Yu and Y. H. Tang. 2010. Statistical downscaling of daily precipitation using support vector machines and multivariate analysis. Journal of Hydrology, 385(1-4): 13-22.
Devak, M., C. T. Dhanya and A. K.  Gosain. 2015. Dynamic coupling of support vector machine and K-nearest neighbour for downscaling daily rainfall. Journal of Hydrology, 525: 286-301.
Du, P., J. Wang, W. Yang and T. Niu. 2018. Multi-step ahead forecasting in electrical power system using a hybrid forecasting system.Renewable Energy, 122: 533-550.
Friedman, J.H. 1991. Multivariate adaptive regression splines. The Annals of Statistics, 19(1): 1-67.
Hadi, S. J and M. Tombul. 2018. Streamflow forecasting using four wavelet transformation combinations approaches with data-driven models: A comparative study. Water Resources Management, 32(14): 4661-4679.
Harpham, C and R. L. Wilby. 2005. Multi-site downscaling of heavy daily precipitation occurrence and amounts. Journal of Hydrology, 312(1-4): 235-255.
Kundu, S., D. Khare and A. Mondal. 2017. Future changes in rainfall, temperature and reference evapotranspiration in the central India by least square support vector machine. Geoscience Frontiers, 8(3): 583-596.‏
Lee, K. T., Hung, W. C and Meng, C. C. 2008. Deterministic insight into ANN model performance for storm runoff simulation. Water Resources Management. 22(1), 67-82.
Mekanik, F., M. A. Imteaz, S. Gato-Trinidad and A. Elmahdi. 2013. Multiple regression and Artificial Neural Network for long-term rainfall forecasting using large scale climate models. Journal of Hydrology. 503:11–21.
Mirjalili, S and A. Lewis. 2016. The whale optimization algorithm. Advances in Engineering Software, 95: 51-67.
Modaresi, F., S. Araghinejad and K. Ebrahimi. 2018. A comparative assessment of artificial neural network, generalized regression neural network, least-square support vector regression, and k-nearest neighbor regression for monthly streamflow forecasting in linear and nonlinear conditions. Water Resources Management, 32(1), 243-258.
Mujumdar, P. P and S. Ghosh. 2008. Modeling GCM and scenario uncertainty using a possibilistic approach: Application to the Mahanadi River, India. Water Resources Research, 44(6): W06407.
Nourani, V., A. H. Baghanam and H. Gokcekus. 2018. Data-driven ensemble model to statistically downscale rainfall using nonlinear predictor screening approach. Journal of Hydrology, 565: 538-551.‏
Quinlan, J. R. 1992. Learning with continuous classes, In 5th Australian Joint Conference on Artificial Intelligence, 92, 343-348.
 
Rezaie-Balf, M., Z. Zahmatkesh and S. Kim. 2017. Soft computing techniques for rainfall-runoff simulation: local non–parametric paradigm vs. model classification methods. Water Resources Management, 31(12): 3843-3865.
Singh, K. K., M. Pal and V. P. Singh. 2010. Estimation of mean annual flood in Indian catchments using backpropagation neural network and M5 model tree. Water Resources Management 24(10): 2007-2019.
Suykens, J. A. 2001. Nonlinear modelling and support vector machines. 2001. In Instrumentation and Measurement Technology Conference. Proceedings of the 18th IEEE 1, 287-294.
Tavakol-Davani, H., M. Nasseri and B. Zahraie. 2013. Improved statistical downscaling of daily precipitation using SDSM platform and data-mining methods. International Journal of Climatology, 33(11): 2561-2578.
Wilby, R. L., L. E. Hay and G. H. Leavesley. 1999. A comparison of downscaled and raw GCM output: Implications for climate change scenarios in the San Juan River basin, Colorado. Journal of Hydrology, 225(1-2): 67-91.
Wu. C. L., K. W. Chau and C. Fan. 2010. Prediction of rainfall time series using Modular Artificial Neural Networks coupled with data preprocessing techniques. Journal of Hydrology, 389: 146–167

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