Suitability of Neural Network Techniques for Rainfall-Runoff Modelling Over a River Basin: A Comprehensive Literature Review

Sanjeev Karmakar*, Pradeep Kumar Mishra **, Shreerup Goswami ***
* Bhilai Institute of Technology, Durg, Chhattisgarh, India
** Research Scholar, Bhilai Institute of Technology, Durg, Chhattisgarh, India
*** Professor of Geology, Department of Earth Sciences, Sambalput University Burla, Odisha, India..
Periodicity:May - July'2018


Rainfall-Runoff (R-R) modelling over a river basin is a complex and challenging task for a hydrological scientist. It is almost complicated due to chaos behaviour of R-R data time series. However, since 1972 various techniques are suggested by the world hydrological scientists and shown some extent of success. In this study, a comprehensive review of the models of R-R modelling over river basin from 1972 to 2016 was performed, wherein models of various contribute is studied year-wise. As an outcome, support vector machine and soft-computing, i.e., neural network, fuzzy logic, and genetic algorithm techniques based on statistics have been found to be successfully applied. In numerical modelling, two different architectures of neural network such as BPN and RBF were found more suitable. The BPN was better evaluated over RBF architecture as far as performance and complexity of implementation is concerned. Finally, it is concluded that BPN is sufficient enough to resolve this complex problem since it has shown 80% certainty in prediction of R-R over different river basins. However, obtaining optimum architecture for better performance is mandatory. These evidences are broadly discussed in this review article.


Rainfall-Runoff, Modelling, Forecasting, Neural Network, BPN, Back-propagation

How to Cite this Article?

Karmakar, S., Mishra, P.K., & Goswami, S. (2018). Suitability of Neural Network Techniques for Rainfall-Runoff Modelling Over a River Basin: A Comprehensive Literature Review. i-manager’s Journal on Future Engineering and Technology, 13(4), 68-83.


[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 modelling approach. J. Hydrol. Engg., 16(4), 384- 390.
[2]. Agarwal, A., & Singh, R. D. (2004). Runoff modelling through back propagation artificial neural network with variable Rainfall-Runoff data. Water Reso. Manage., 18, 285-300.
[3]. Aichouri, I., Hani, A., Bougherira, N., Djabri, L., Chaffai, H., & Lallahem, S. (2015). River flow model using artificial neural networks. Energy Procedia, Elsevier, 74, 1007-1014.
[4]. Anmala, J., Zhang, B., & Govindraju, R. S. (2000). Comparison of ANNs and empirical approaches for predicting Wa-tershed Runoff. J. Water Res. Planning and Manag., ASCE, 126(3), 156-166.
[5]. Aytek, A., Asce, M., & Alp, M. (2008). An application of artificial intelligence for rainfall–runoff modelling. J. Earth Syst. Sci., 117(2), 145-155.
[6]. Bach, H., Lampart, G., Strasser, G., & Mauser, W. (1999). First results of an integrated flood forecast system based on remote sensing data. IEEE Conference Publications Geoscience and Remote Sensing Symposium, IGARSS '99 Proceedings (pp. 864-866).
[7]. Baumgartner, M. F., Seidel, K., & Martinec, J. (1987). Toward snowmelt runoff forecast based on multisensor remote-sensing information. IEEE Trans. Geosci. and Remot. Sens., 25(6), 746-750.
[8]. Cannas, B., Fanni, A., Pintusb, M., & Sechib, G. M. (2002). Neural network models to forecast hydrological risk. IJCNN '02, Proceedings of the International Joint Conference on Neural Networks, IEEE Conference Publications, (Vol. 1, pp. 623-626).
[9]. Carriere, P., Mohagheghs, S., & Gaskari, R. (1996). Performance of a virtual runoff hydrograph system. J. Wat. Resour. Plan. Manage. ASCE, 122(6), 421-427.
[10]. Chakraborty, K., Mehrotra, K., Mohan, C. K., & Ranka, S. (1992). Neural networks and their applications. Review of Scientific Instruments, 65, 1803-1832.
[11]. Chang, F. J., Liang, J. M., & Chen, Y. C. (2001). Flood forecasting using radial basis function neural networks. IEEE Trans., 31(4), 530-535.
[12]. Chen, S. M., Wang, Y. M., & Tsou, I. (2013). Using artificial neural network approach for modelling rainfallrunoff due to typhoon. J. of Earth Syst. Sci., 122(2), 399- 405.
[13]. Cheng, C. T., Chau, C. W., & Li, X. Y. (2007). Hydrologic uncertainty for bayesian probabilistic forecasting model based on BP ANN. Third International Conference on Natural Computation (ICNC 2007), IEEE Conference Publications, (Vol. 1, pp. 197-201).
[14]. Chibanga, R., Berlamont, J., & Vandewalle, J. (2003). Modelling and forecasting of hydrological variables using artificial neural networks: The Kafue river sub-basin. Hydrol. Sci. J., 48(3), 363-379.
[15]. Corani, G., & Guariso, G. (2005). Coupling fuzzy modelling and neural networks for river flood prediction. IEEE Trans. Syst. Man and Cybernet., 35(3), 382-390.
[16]. Dawson, C. W., & Wilby, R. (1998). An artificial neural network approach to rainfall runoff modelling. Hydrol. Sci. J., 43(1), 47-68.
[17]. Demirel, M. C., Booij, M. J., & Kahya, E. (2012). Validation of an ANN flow prediction model using a multistation cluster analysis. J. Hydrol. Engg., 17(2), 262- 271.
[18]. Deshmukh, R. P., & Ghatol, A., (2010). Comparative study of Jorden and Elman model of neural network for short term flood forecasting. 3rd IEEE International Conference on Computer Science and Information Technology (ICCSIT), IEEE Conference Publications (Vol. 9, pp. 400-404).
[19]. Dibike, Y. B., & Solomatine, D. P. (1999). River flow forecasting using Artificial Neural Networks. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 26(1), 1-7.
[20]. Duc, L. V. (2009). Applicability of Artificial Neural Network model for simulation of monthly runoff in comparison with some other traditional models. Sci. and Tech. Develop., 12(4), 94-106.
[21]. El-shafie, A., Mukhlisin, M., Najah, A. A., & Taha, M. R. (2011). Performance of artificial neural network and regression techniques for rainfall-runoff prediction. Int. J. of Phy. Sci., 6(8), 1997-2003.
[22]. Fernando, D. A. K., & Jayawardena, A. W. (1998). Runoff forecasting using RBF networks with OLS algorithm. J. Hydrol. Engg., 3(3), 203-209.
[23]. French, M. N., Krajewski, W. F., & Cuykendall, R. R. (1992). Rainfall forecasting in space and time using a neural network. J. Hydrol., 137, 1-31.
[24]. Gaume, E., & Gosset, R. (2003). Overparameterisation, a major obstacle to the use of artificial neural networks in hydrology. Hydrol. Earth Syst. Sci., 7, 693-706.
[25]. Ghumman, A. R., Ghazaw, Y. M., Sohail, A. R., & Watanabe, K. (2011). Runoff forecasting by artificial neural network and conventional model. Alex. Engg. J., 50, 345-350.
[26]. Grayson, R. B., Moore, I. D., & McMahon, T. A. (1992). Physically Based Hydrologic-2. Is the Concept Realistic? Water Resources Research, 28(10), 2659-2666.
[27]. Halff, A. H., & Azmoodeh, H. M. (1993). Predicting Runoff from rainfall using neural networks. In Kuo, C. Y. (Ed.), Engineering Hydrology (pp. 760-765). Proceedings of the Symposium sponsored by the Hydraulics Division of ASCE, San Francisco, CA, ASCE, New York.
[28]. Hammerstrom, D. (1993). Neural Networks at Work. IEEE Spectrum, 30(7), 1993, 46-53.
[29]. Hsu, K. L., Gupta, H. V., & Sorooshian, S. (1995). Artificial neural network modelling of the rainfall-runoff process. Wat. Resour. Res., 31(10), 2517-2530.
[30]. Huang, M., & Tian, Y., (2010). Design and implementation of a visual modelling tool to support interactive runoff forecasting. IEEE International Conference on Date Intelligent Computing and Intelligent Systems (ICIS) (pp. 270-274). IEEE.
[31]. Hung, N. Q., Babel, M. S., Weesakul, S., & Tripathi, N. K. (2009). An artificial neural network model for rainfall forecasting in Bangkok, Thailand. Hydrol. Earth Syst. Sci., 13, 1413-1425.
[32]. llker, A., Kose, M., Ergin G., & Terzi, &o; (2011). An Artificial Neural Networks approach to monthly flow estimation. International Symposium on Innovations in Intelligent Systems and Applications (INISTA), IEEE Conference Publication (pp. 325-328).
[33]. Imrei, C. E., Durucan, S., & Korre, A. (2000). River flow prediction using Artificial Neural Networks: Generalization beyond calibration range. J. Hydrol., 233, 138-153.
[34]. Jingbo, L., Zengchuan, D., Dezhi. W., & Shaohua. L. (2008). Research on Runoff Forecast Model based on Phase Space Reconstruction. World Congress on Intelligent Control and Automation, WCICA 2008 (pp. 5339-5343). IEEE.
[35]. Ju. Q.,Yu. Z., Hao. Z., Zhu, C., & Liu, D. (2007). Hydrologic simulations with Artificial Neural Networks. Third International Conference on Natural Computation (ICNC 2007), (pp. 22-27). IEEE.
[36]. Kaltech, M. A. (2008). Rainfall-Runoff modelling using artificial neural network modelling and understanding. Caspian J. of Envir. Sci., 6, 153-158.
[37]. Karmakar, S., Shrivastava, G., & Kowar, M. K. (2014). Impact of learning rate and momentum factor in the performance of back-propagation neural network to identify internal dynamics of chaotic motion, Kuwait. J. Sci. 41(2), 151-174.
[38]. Kisi, O. (2007). Streamflow forecasting using different Artificial Neural Network Algorithms. J. Hydrol. Engg., American Society of Civil Engineers, 12(5), 532-539.
[39]. Kitanidis, P. K., & Bras, R. L. (1979). Collinearity and stability in the estimation of rainfall-runoff model parameters. J. Hydrol., 42, 91-108.
[40]. Kumar, P. S., Praveen, T. V., & Anjanaya, M. P. (2016). Artificial Neural Network model for Rainfall-Runoff -A case study. Int. J. of Hybrid Info. Tech., 9(3), 263-272.
[41]. Li, C., & Yuan, X. (2008). Research and Application of Data Mining for Runoff Forecasting. Intelligent Computation Technology and Automation (ICICTA). International Conference on (pp. 795-798). IEEE.
[42]. Li, K., Ji, C., Zhang, Y., Xie, W., & Zhang, X. (2012). Study of mid and long-term runoff forecast based on back-propagation neural network. International Conference on Industrial Control and Electronics Engineering (ICICEE), IEEE Conference Publications (pp. 188-191).
[43]. Lin, J. Y., Cheng, C. T., & Chau, K. W. (2006). Using support vector machines for long-term discharge Prediction. Hydrol. Sci. J., 51(4), 599-612.
[44]. Linke, H., Karimanzira, D., Rauschenbach, T., & Pfutzenreuter, T. (2011). Flash flood prediction for small rivers. International Conference on Networking, Sensing and Control (ICNSC), IEEE Conference Publications (pp. 86-91).
[45]. Liu, F., Jiang, D., Fu, B., & Zhou, J. (2008). Nonlinear Forecast modelling based on Wavelet Analysis. International Conference on Computer Science and Software Engineering, IEEE Conference Publications (Vol. 1, pp. 622-625).
[46]. Lorenz, E. (1972). Predictability: Does the flap of a butterfly's wings in Brazil set off a tornado in Texas. Resonance – Journal of Science Education, 20(3), 260- 263.
[47]. Lorrai, M., & Sechi, G. M. (1995). Neural nets for modelling rainfall-runoff transformations. Wat. Res. Manag., 9, 299-313.
[48]. Martinec, J. (1982). Runoff Modelling from Snow Covered Area. IEEE Trans. Geosci. and Remot. Sens., 20(3), 259-262.
[49]. Minns, A. W., & Hall, M. J. (1996). Artificial neural networks as rainfall-runoff models. Hydrol. Sci. J., 41(3), 399-417.
[50]. Mittal, P., Chowdhury, S., Roy, S., Bhatia, N., & Srivastav, R. (2012). Dual Artificial Neural Network for Rainfall-Runoff Forecasting. J. of Water Res. and Protect., 4, 1024-1028.
[51]. Modarres, R. (2009). Multi-Criteria Validation of Artificial Neural Network Rainfall-Runoff. Hydrol. and Earth Syst. Sci., 13, 411-421.
[52]. Phuphong, S., & Surussavadee, C. (2013). An Artificial Neural Network based Runoff Forecasting Model in the absence of precipitation data: A Case study of Khlong U-Tapao river basin, Songkhla Province, Thailand. 4th International Conference on Intelligent Systems, Modelling and Simulation (pp. 73-77). IEEE.
[53]. Isa, M. H., & Rezaur, R. B. (2015). Artificial Neural Networks Modelling in water resources engineering: Infrastructure and applications, world academy of science, engineering and technology. Int. J. of Civil, Environ., Struct., Construct. and Architect. Engg. 6(2), 128-136.
[54]. Patil, S., & Walunjkar. (2013). Rainfall-Runoff forecasting techniques for avoiding global warming. International Conference on Information Communication and Embedded Systems (ICICES), (pp. 1220-1223). IEEE.
[55]. Peter, K. K. (1980). Adaptive Filtering through detection of isolated transient error in rainfall-runoff models. Water Resources Research, 16, 740-748.
[56]. Rajurkar, M. P., Kothyari, U. C., & Chaube, U. C. (2004). Modelling of the daily rainfall-runoff relationship with artificial neural network. J. Hydrol., 285, 96-113.
[57]. Rumelhart, D., Hinton, G. E., & Williams, R. J. (1986). Learning Internal Representation By Error Propagation. R J Parallel Distributed Processing: Exploration in the Microstructure of Cognition. MIT Press Cambridge, 318- 362.
[58]. Sahai, A. K., Soman, M. K., & Satyan, V. (2000). All India summer monsoon rainfall prediction using an artificial neural network. Climate Dynamics, 16, 291-302.
[59]. Sarkar, A., & Kumar, R. (2012). Artificial Neural Networks for Event Based Rainfall-Runoff Modelling. Journal of Water Resource and Protection, 4, 891-897.
[60]. Sivapragasam, C., Liong, S. Y., & Pasha, M. F. K. (2001). Rainfall and runoff forecasting with SSA-SVM approach. J. Hydroinfo., 3(3), 141-152.
[61]. Smith, J., & Eli, R. N. (1995). Neural network models of rainfall-runoff process. J. Wat. Resour. Plan. Manage., ASCE, 121(6), 499-508.
[62]. Solaimani, K. (2009). Rainfall-runoff Prediction Based on Artificial Neural Network (A Case Study: Jarahi Watershed). American-Eurasian J. Agric. and Environ. Sci. 5(6), 856-865.
[63]. Sun, X. L., Tan, Y. M., & Xu, X. C. (2008). BP Neural Network Model based on reconstruction phase space and its application in runoff forecasting. International Conference on Computer Science and Software Engineering, (Vol. 4, pp. 794-797). IEEE.
[64]. Tayfur, G., Vijay, Singh, V. P., & Asce, F. (2006). ANN and Fuzzy Logic Models for simulating Event-based Rainfall-Runoff. J. Hydraulic Engg., 132(12), 1321-1330.
[65]. Tayyab, M., Zhou, J., Zeng, X., & Adnan, R. (2016). Discharge forecasting by applying artificial neural networks at the Jinsha river basin China. Euro. Sci. J., 12(9), 108-127.
[66]. Tokar, A. S., & Markus, M. (2000). Precipitation runoff modelling using artificial neural networks and conceptual models. J. Hydrol. Engg., 5(2), 156-161.
[67]. Tokar, A. Z., & Johnson, P. A. (1999). Rainfall-runoff modelling using artificial neural network. J. Hydrol. Engg., 4(3), 232-239.
[68]. Vandewiele, G. L., & Yu, C. (1992). Methodology and comparative study of monthly water balance models in Belgium, China and Burma. J. Hydrol., 134(1-4), 315-347.
[69]. Wu, C. L., & Chau, K. W. (2011). Rainfall-Runoff Modelling using Artificial Neural Network coupled with Singular Spectrum Analysis. Journal of Hydrology, 399(3- 4), 394-409.
[70]. Xia, J., O'Connor, K. M., Kachroo, R. K., & Liang, G. C. (1997). A non-linear perturbation model considering catchment wetness and its application in fiver flow forecasting. J. Hydrol., 200, 164-178.
[71]. Xu, J., Zhao, J., Zhang, W., Hu, Z., & Zheng, Z. (2009). Mid-Short-Term daily Runoff forecasting by ANNs and multiple process-based Hydrological models. IEEE Youth Conference on Information, Computing and Telecommunication, YC-ICT'09, (pp. 526-529). IEEE.
[72]. Yan, J., Chen, S., & Jiang, C. (2010). The application of BP and RBF Model in the forecasting of the runoff and the sediment transport volume in Linjin Section. Sixth International Conference on Natural Computation, (Vol. 4, pp. 1892-1896).
[73]. Zhang, B., & Govindaraju, S. (2000). Prediction of watershed runoff using Bayesian Concepts and Modular Neural Networks. Water Resources Research, 36(3), 753- 762.
[74]. Zhu, M., Fujita, M., & Hashimoto, N. (1994). Application of Neural Networks to Runoff Prediction. In Hipel, K. W. et al., (Eds), Stochastic and Statistical Method in Hydrology and Environmental Engineering (pp. 205- 216). Kluwer, Dordrecht.

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