Comparative Analysis of Rainfall-Runoff Modeling Using Support Vector Machines for Two Dams in Uttarakhand


  • Basant Ballabh Dumka Department of Soil & Water Conservation Engineering, G.B. Pant University of Agriculture and Technology
  • Pravendra Kumar Department of Soil & Water Conservation Engineering, G.B. Pant University of Agriculture and Technology



The main objective of this study was to evaluate and compare the performances of rainfall-runoff models that were developed by using support vector machines (SVMs). Rainfall and runoff data of Haripura and Baur dams were adopted on daily basis from Irrigation Division Rudrapur in Uttarakhand. In this study, radial kernel function was used. As the values of Cost function (C),  and  varies, performances of the models can be altered. So, at optimum values of these variables, there exists a best correlation between rainfall and runoff. It can be inferred from the study that SVM models provide satisfactory results for both dams. These results can be used for runoff prediction for various purpose such as irrigation etc.


Support Vector Machines, Radial Kernal Function, Pooled Average Relative Error


Download data is not yet available.


P. G. Shinde, “WATER SCENARIO 2025,” in National Level Conference on Water Management Scenario 2025 Problems, Issues and Challenges. Accessed: Jan. 09, 2023. [Online]. Available:

M. Balamurugan, K. S. Kasiviswanathan, I. Ilampooranan, and B. S. Soundharajan, “COVID-19 Lockdown Disruptions on Water Resources, Wastewater, and Agriculture in India,” Frontiers in Water, vol. 3, p. 24, Mar. 2021, doi: 10.3389/FRWA.2021.603531/BIBTEX.

M. J. Neal, “COVID-19 and water resources management: reframing our priorities as a water sector,” Water Int, vol. 45, no. 5, pp. 435–440, Jul. 2020, doi: 10.1080/02508060.2020.1773648.

Y. Li, G. Li, L. Guo, L. Hernández-Callejo, S. Nesmachnow, and S. G. Saavedra, “Feature Selection for Regression Based on Gamma Test Nested Monte Carlo Tree Search,” Entropy 2021, Vol. 23, Page 1331, vol. 23, no. 10, p. 1331, Oct. 2021, doi: 10.3390/E23101331.

V. Güldal and H. Tongal, “Comparison of recurrent neural network, adaptive neuro-fuzzy inference system and stochastic models in eg̈irdir lake level forecasting,” Water Resources Management, vol. 24, no. 1, pp. 105–128, Jan. 2010, doi: 10.1007/S11269-009-9439-9.

B. M. Gupta, S. M. Dhawan, and G. M. Mamdapur, “Support Vector Machine (SVM) Research in India: A Scientometric Evaluation of India’s Publications Output during 2002-19,” The Journal of Indian Library Association, vol. 57, no. 3, pp. 12–25, 2021.

B. Pang, S. Guo, L. Xiong, and C. Li, “A nonlinear perturbation model based on artificial neural network,” J Hydrol (Amst), vol. 333, no. 2–4, pp. 504–516, Feb. 2007, doi: 10.1016/J.JHYDROL.2006.09.015.

T. Roshni, M. K. Jha, and J. Drisya, “Neural network modeling for groundwater-level forecasting in coastal aquifers,” Neural Comput Appl, vol. 32, no. 16, pp. 12737–12754, Aug. 2020, doi: 10.1007/S00521-020-04722-Z/METRICS.

Z. Hassan et al., “Comparison of Artificial Neural Network and Support Vector Machine for Long-Term Runoff Simulation,” Earth Environ. Sci., vol. 476, no. 1, p. 012119, Jun. 2020, doi: 10.1088/1755-1315/476/1/012119.

M. Bray and D. Han, “Identification of support vector machines for runoff modelling,” Journal of Hydroinformatics, vol. 6, no. 4, pp. 265–280, Oct. 2004, doi: 10.2166/HYDRO.2004.0020.

Y. Han, S.-L. Lau, M. Kayhanian, and M. K. Stenstrom, “Characteristics of Highway Stormwater Runoff,” Water Environment Research, vol. 78, no. 12, pp. 2377–2388, Nov. 2006, doi: 10.2175/106143006X95447.

F. Sedighi, M. Vafakhah, and M. R. Javadi, “Rainfall–Runoff Modeling Using Support Vector Machine in Snow-Affected Watershed,” Arab J Sci Eng, vol. 41, no. 10, pp. 4065–4076, Oct. 2016, doi: 10.1007/S13369-016-2095-5/METRICS.

A. Tripathi, S. Maithani, and S. Kumar, “X-band persistent SAR interferometry for surface subsidence detection in Rudrapur City, India,” in Proc. SPIE 10793, Remote Sensing Technologies and Applications in Urban Environments III, Oct. 2018, vol. 10793, pp. 105–115. doi: 10.1117/12.2326267.

V. Vapnik, “The Support Vector Method of Function Estimation,” in Nonlinear Modeling, J. A. K. Suykens and J. Vandewalle, Eds. Springer, Boston, MA, 1998, pp. 55–85. doi: 10.1007/978-1-4615-5703-6_3.

T. Evgeniou and M. Pontil, “Support vector machines: Theory and applications,” in Machine Learning and Its Applications, vol. 2049 LNAI, Springer Verlag, 2001, pp. 249–257. doi: 10.1007/3-540-44673-7_12/COVER.

M. Kumar, P. Kumar, A. Kumar, A. Elbeltagi, and A. Kuriqi, “Modeling stage–discharge–sediment using support vector machine and artificial neural network coupled with wavelet transform,” Appl Water Sci, vol. 12, no. 5, May 2022, doi: 10.1007/S13201-022-01621-7.






Research Article

How to Cite

B. B. Dumka and P. Kumar, “Comparative Analysis of Rainfall-Runoff Modeling Using Support Vector Machines for Two Dams in Uttarakhand”, J. Mod. Sim. Mater., vol. 5, no. 1, pp. 7–20, Dec. 2022.