Support Vector Machine and Neural Network based Model for Monthly Stream Flow Forecasting

  • Authors

    • Nuratiah Zaini
    • Marlinda Abdul Malek
    • Marina Yusoff
    • Siti Fatimah Che Osmi
    • Nurul Hani Mardi
    • Shuhairy Norhisham
  • Bat algorithm, Forecasting, Optimization, Streamflow, Support vector machine
  • Accurate forecasting of streamflow is desired in many water resources planning and management, flood prevention and design development. In this study, the accuracy of two hybrid model, support vector machine - particle swarm optimization (SVM-PSO) and bat algorithm – backpropagation neural network (BA-BPNN) for monthly streamflow forecasting at Kuantan River located in Peninsular Malaysia are investigated and compared to regular SVM and BPNN model. Heuristic optimization namely PSO and BA are introduced to find the optimum SVM and BPNN parameters. The input parameters to the forecasting models are antecedent streamflow, historical rainfall and meteorological parameters namely evaporation, temperature, relative humidity and mean wind speed. Two performance evaluation measure, root mean square error (RMSE) and coefficient of determination (R2) were employed to evaluate the performance of developed forecasting model. It is found that, RMSE and R2 for hybrid SVM-PSO are 24.8267 m3/s and 0.9651 respectively while general SVM model yields RMSE of 27.5086 m3/s and 0.9305 of R2 for testing phase. Besides that, hybrid BA-BPNN produces RMSE, 17.7579 m3/s and R2, 0.7740 while BPNN model produces lower RMSE and R2 of 28.1396 m3/s and 0.5015 respectively. Therefore, the results indicate that hybrid model, SVM-PSO and Bat-BPNN yield better performance as compared to general SVM and BPNN, respectively in streamflow forecasting.

  • References

    1. [1] B. Xing, R. Gan, G. Liu, Z. Liu, J. Zhang and Y. Ren, "Monthly Mean Streamflow Prediction Based on Bat Algorithm-Support Vector Machine," Journal of Hyrologic Engineering, vol. 21, no. 2, 2016.

      [2] Sudheer, R. Maheswaran , B. K. Panigrahi and S. Mathur, "A Hybrid SVM-PSO Model for Forecasting Monthly Streamflow," Neural Computing and Application, vol. 24, pp. 1381-1389, 2013.

      [3] R. Ramesan, M. A. Shamim, D. Han and J. Mathew, "Runoff prediction using an integrated hybrid modelling scheme," Journal of Hydrology, vol. 372, no. 1-4, pp. 48-60, 2009.

      [4] S. M. Chen, Y. M. Wang and I. Tsou, "Using Artificial Neural Network Approach for Modeling Rainfall-Runoff due to Typhoon," Journal of Earth System Science, vol. 122, no. 2, pp. 399-405, 2013.

      [5] C. Sivapragasam, S. Vanitha, N. Muttil, K. Suganya, S. Suji, M. T. Selvi, R. Selvi and J. S. Sudha, "Monthly flow forecast for Mississippi River basin using artificial neural networks," Neural Computing and Applications, pp. 1785-1793, 2014.

      [6] D.-H. Lee and D.-S. Kang, "The Application of the Artificial Neural Network Ensemble Model for Simulating Streamflow," Procedia Engineering, vol. 154, pp. 1217-1224, 2016.

      [7] K.-H. Wang and A. Altunkaynak, "Comparative Case Study of Rainfall-Runoff Modeling between SWMM and Fuzzy Logic Approach," Journal of Hydrologic Engineering, vol. 17, no. 2, pp. 283-291, 2012.

      [8] Talei, L. H. C. Chua and C. Quek, "A Novel Of a Neuro-Fuzzy Computational Technique in Event-Based Rainfall-Runoff Modeling," Expert System with Applications, vol. 37, pp. 7456-7468, 2010.

      [9] D. Mehr, E. Kahya and E. Olyaie, "Streamflow Prediction Using Linear Genetic Programming in Comparison with Neuro-Wavelet Technique," Journal of Hydrology, pp. 240-249, 2013.

      [10] D. Mehr, E. Kahya and C. Yerdelen, "Linear genetic programming application for successive-station monthly streamflow prediction," Computer & Geoscience, pp. 63-72, 2014.

      [11] D. A. K. Fernando, A. Y. Shamseldin and R. J. Abrahart, "Using Gene Expression Programming to Develop a Combined Runoff Estimate Model from Conventional Rainfall-Runoff Model Outputs," in 18th World IMACS / MODSIM Congress, Cairns, Australia, Cairns, 2009.

      [12] H. Chu, J. Wei, T. Li and K. Jia, "Application of Support Vector Regression for Mid- and Long-term Runoff Forecasting in “Yellow River Headwater†Region," Procedia Engineering, vol. 154, pp. 1251-1257, 2016.

      [13] J. Guo, J. Zhou, H. Qin, Q. Zou and Q. Li, "Monthly streamflow forecasting based on improved support vector machine," Expert System with Applications, pp. 13073-13081, 2011.

      [14] C.-h. Hu, Z.-n. Wu, J.-j. Wang and Lina-Liu, "Application of the Support Vector Machine on Precipitation-Runoff Modeling in Fenhe River," in Water Resource and Environmental Protection (ISWREP), 2011 Intenational Symposium on, Xi'an, 2011.

      [15] S. Londhe and S. S. Gavraskar, "Forecasting One Day Ahead Stream Flow Using Support Vector Regression," Aquatic Procedia, vol. 4, pp. 900-907, 2015.

      [16] M. A. Ghorbani, R. Khatibi, A. D. Mehr and H. Asadi, "Chaos-based multigene genetic programming: A new hybrid strategy for river flow forecasting," Journal of Hydrology, vol. 562, pp. 455-467, 2018.

      [17] G. B. Humprey, M. S. Gibbs, G. C. Dandy and H. R. Maier, "A hybrid approach to monthly streamflow forecasting: Integrating hydrological model outputs into a Bayesian artificial neural network," Journal of Hydrology, vol. 540, pp. 623-640, 2016.

      [18] P. G. Nieto, E. Garcia-Gonzalo, J. A. Fernandez and C. D. Muniz, "Hybrid PSO-SVM-based method for long term forecasting of turbidity in the Nalon river basin: A case study in Nothern Spain," Ecology Engineering, pp. 192-200, 2014.

      [19] V. Nourani, A. H. Baghanam, J. Adamowski and O. Kisi, "Applications of hybrid wavelet–Artificial Intelligence models in hydrology: A review," Journal of Hydrology, pp. 358-377, 2014.

      [20] J. Veintimilla-Reyes, F. Cisneros and P. Vanegas, "Artificial Neural Networks Applied to Flow Prediction: A Use Case for the Tomebamba River," Porcedia Engineering, vol. 162, pp. 153-161, 2016.

      [21] K. Kasiviswanathan, J. He, K. Sudheer and J.-H. Tay, "Potential application of wavelet neural network ensemble to forecast streamflow for flood management," Journal of Hydrology, vol. 536, pp. 161-173, 2016.

      [22] W.-C. Wang, K.-W. Chau, C.-T. Cheng and L. Qiu, "A Comparison of Performance of Several Artificial Intelligence Methods for Forecasting Monthly Discharge Time Series," Journal of Hydrology, vol. 374, no. 3-4, pp. 294-306, 2009.

      [23] G.-F. Lin, G.-P. Chen, P.-Y. Huang and Y.-C. Chou, "Support vector machine-based models for hourly reservoir inflow forecasting during typhoon-warning periods," Journal of Hydrology, pp. 17-29, 2009.

      [24] P. Bento, J. Pombo, M. Calado and S. Mariano, "A Bat Optimized Neural Network and Wavelet Transform Approach for Shortterm Price Forecasting," Applied Energy, vol. 210, pp. 88-97, 2018.

      [25] X.-S. Yang, "A New Metaheuristic Bat-Inspired Algorithm," in Natured Inspired Cooperative Strategies for Optimization (NICSO 2010), Poland, Springer, 2010, pp. 65-74.

      [26] H. Dehgani and B. Dejan, "Copper Price Estimation using Bat Algorithm," Resource Policy, vol. 55, pp. 55-61, 2018.

      [27] H. Banati and R. Chaudhary, "Multi-Modal Bat Algorithm with Improved Search (MMBAIS)," Journal of Computational Science, vol. 23, pp. 130-144, 2017.

      [28] W.-c. Wang, D.-m. Xu, K.-w. Chau and S. Chen, "Improved Annual Rainfall-Runoff Forecasting Using PSO-SVM Model Based on EEMD," Journal of Hydroinfomatics, pp. 1377-1390, 2013.

      [29] W. Xuan, L. V. Jiake and X. Deti, "A hybrid approach of support vector machine with particle swarm optimization for water quality prediction," The 5th International conference on computer science & education Hefei, China, pp. 1158-1163, 2010.

      [30] M. K. Ramawan, Z. Othman , S. I. Sulaiman , I. Musirin and N. Othman, "A Hybrid Bat Algorithm Artificial Neural Network for Grid- Connected Photovoltaic System Output Prediction," in 2014 IEEE 8th International Power Engineering and Optimization Conference (PEOCO2014), Malaysia, 2014.

      [31] M. A. Al-Betar, M. A. Awadallah, H. Faris, X.-S. Yang, A. T. Khader and O. A. Alomari, "Bat-inspired algorithms with natural selection mechanisms for global optimization," Neurocomputing, vol. 273, pp. 448-465, 2018.

      [32] V. N. Vapnik, The nature of statistical learning theory, New Yok,USA: Springer, 1995.

      [33] N. S. Raghavendra and P. C. Deka, "Support Vector Machine Application in the Field Hydrology: Areview," Applied Soft Computing, pp. 372-386, 2014.

      [34] V. N. Vapnik, Statistical learning theory, New York: Wiley, 1998.

      [35] N. Zaini, M. A. Malek, M. Yusoff, N. H. Mardi and S. Norhisham, "Daily River Flow Forecasting with Hybrid Support Vector Machine – Particle Swarm Optimization," in IOP Conf. Series: Earth and Environmental Science, Malaysia, 2018.

      [36] Gershenson, "Artificial Neural Networks for Beginners," Cornell University Library, 2003.

      [37] Z. He, X. Wen, H. Liu and J. Du, "A comparative study of artificial neural network, adaptive neuro fuzzy inference system and support vector machine for forecasting river flow in the semiarid mountain region," Journal of Hydrology, pp. 379-386, 2014.

      [38] P. Engelbrecht, Fundamentals of Computational Intelligence, England: Wiley, 2005.

      [39] P. Engelbrecht, Computational Intelligence An Introduction, England: Wiley, 2007.

  • Downloads

  • How to Cite

    Zaini, N., Abdul Malek, M., Yusoff, M., Che Osmi, S. F., Hani Mardi, N., & Norhisham, S. (2018). Support Vector Machine and Neural Network based Model for Monthly Stream Flow Forecasting. International Journal of Engineering & Technology, 7(4.35), 683-688.