i-manager Publications

Performance Analysis of Soft-Computing Techniques in Rainfall-Runoff Modeling over a River Basin

Sanjeev Karmaka *, Pradeep Kumar Mishr**, Shreerup Goswami***

*-** Bhilai Institute of Technology, Durg, Chhattisgarh, India.

***Sambalpur University, Burla, Odisha, India.

Periodicity:August - October'2021
DOI : https://doi.org/10.26634/jfet.17.1.17454

Abstract

Rainfall-Runoff (R-R) modelling is a challenging and operational task for hydrological scientists. For the past few years, the scientific community has been suggesting soft computing techniques to solve this problem. In this study, various types of soft computing such as Back Propagation Neural Network (BPN), Radial Basis Function (RBF), Support Vector Machine (SVM), Fuzzy Logic (FL) and Genetic Algorithm (GA) were analyzed systematically. BPN and RBF were found to be more suitable to address R-R modeling, but BPN proved to be more appropriate than RBF. During the test performance, it was observed that the average deviation of R-R from the actual was 23 (% of LPA) while RBF produced 43.6 (% of LPA). All these facts have been described in this paper.

Keywords

Rainfall-Runoff (R-R), Long Period Average (LPA), Back Propagation Neural Network (BPN), Radial Basis Function (RBF), Support Vector Machine (SVM), Genetic Algorithm (GA), Fuzzy Logic (FL), Soft Computing Techniques, River Basin.

How to Cite this Article?

Karmaka, S., Mishr, P. K., and Goswami, S. (2021). Performance Analysis of Soft-Computing Techniques in Rainfall-Runoff Modeling over a River Basin. i-manager’s Journal on Future Engineering & Technology, 17(1), 10-15. https://doi.org/10.26634/jfet.17.1.17454

References

[1]. Abudu, S., King, J. P., & Bawazir, A. S. (2011). Forecasting monthly streamflow of spring-summer runoff season in Rio Grande headwaters basin using stochastic hybrid modeling approach. Journal of Hydrologic Engineering, 16(4), 384-390.

[2]. Aichouri, I., Hani, A., Bougherira, N., Djabri, L., Chaffai, H., & Lallahem, S. (2015). River flow model using artificial neural networks. Energy Procedia, 74, 1007- 1014.

[3]. Aiyun, L., & Jiahai, L. (2011, August). Forecasting monthly runoff using wavelet neural network model. In 2011, International Conference on Mechatronic Science, Electric Engineering and Computer (MEC) (pp. 2177-2180). IEEE.

[4]. Chandwani, V., Agrawal, V., & Nagar, R. (2015). Modeling slump of ready mix concrete using genetic algorithms assisted training of Artificial Neural Networks. Expert Systems with Applications, 42(2), 885-893.

[5]. Duda, R. O., & Hart, P. E. (1973). Pattern classification and scene analysis (Vol. 3). New York: Wiley.

[6]. Huang, M., & Tian, Y. (2010, October). Design and implementation of a visual modeling tool to support interactive runoff forecasting. In 2010, IEEE International Conference on Intelligent Computing and Intelligent Systems (Vol. 3, pp. 270-274). IEEE.

[7]. Noury, M., Khatami, H. R., Moeti, M. T., & Barani, G. (2008). Rainfall-runoff modeling using fuzzy methodology. Journal of Applied Sciences, 8(16), 2851-2858.

[8]. Sattari, M. T., Pal, M., Apaydin, H., & Ozturk, F. (2013). M5 model tree application in daily river flow forecasting in Sohu Stream, Turkey. Water Resources, 40(3), 233-242.

[9]. Sivapragasam, C., Liong, S. Y., & Pasha, M. F. K. (2001). Rainfall and runoff forecasting with SSA–SVM approach. Journal of Hydroinformatics, 3(3), 141-152.

[10]. Wang, W., Xu, D., & Qiu, L. (2010, August). Support vector machine with chaotic genetic algorithms for annual runoff forecasting. In 2010, Sixth International Conference on Natural Computation (Vol. 2, pp. 671- 675). IEEE.

[11]. WRIS. (n.d.). Mahanadi. India Water Resources Information System.

[12]. Xu, J., Zhao, J., Zhang, W., Hu, Z., & Zheng, Z. (2009, September). Mid-short-term daily runoff forecasting by ANNs and multiple process-based hydrological models. In 2009, IEEE Youth Conference on Information, Computing and Telecommunication (pp. 526-529). IEEE.

[13]. Xu, Q., Ren, L., Yu, Z., Yang, B., & Wang, G. (2008, October). Rainfall-runoff modeling at daily scale with artificial neural networks. In 2008, Fourth International Conference on Natural Computation (Vol. 2, pp. 504- 508). IEEE.

If you have access to this article please login to view the article or kindly login to purchase the article

Single Article

	North Americas,UK, Middle East,Europe		India	Rest of world
	USD	EUR	INR	USD-ROW
Online	15	15

ADD TO CART

Options for accessing this content:

If you would like institutional access to this content, please recommend the title to your librarian.
If you already have i-manager's user account: Login above and proceed to purchase the article.
New Users: Please register, then proceed to purchase the article.