Improved Particle Swarm Optimization–Based Artificial Neural Network for Rainfall-Runoff Modeling
This article has a reply.
VIEW THE REPLYThis article has a reply.
VIEW THE REPLYPublication: Journal of Hydrologic Engineering
Volume 19, Issue 7
Abstract
This paper presents the application of an improved particle swarm optimization (PSO) technique for training an artificial neural network (ANN) to predict water levels for the Heshui Watershed, China. Daily values of rainfall and water levels from 1988 to 2000 were first analyzed using ANNs trained with the conjugate gradient, gradient descent, and Levenberg-Marquardt neural network (LM-NN) algorithms. The best results were obtained from the LM-NN, and these results were then compared with those from PSO-based ANNs, including the conventional PSO neural network (CPSONN) and the improved PSO neural network (IPSONN) with passive congregation. The IPSONN algorithm improves PSO convergence by using the selfish herd concept in swarm behavior. The results show that the PSO-based ANNs performed better than the LM-NN. For models run using a single parameter (rainfall) as input, the root mean square error (RMSE) of the testing data set for IPSONN was the lowest (0.152 m) compared to those for CPSONN (0.161 m) and LM-NN (0.205 m). For multiparameter (rainfall and water level) inputs, the RMSE of the testing data set for IPSONN was also the lowest (0.089 m) compared to those for CPSONN (0.105 m) and LM-NN (0.145 m). The results also indicate that the LM-NN model performed poorly in predicting the low and peak water levels in comparison to the PSO-based ANNs. Moreover, the IPSONN model was superior to CPSONN in predicting extreme water levels. Lastly, IPSONN had a quicker convergence rate compared to CPSONN.
Get full access to this article
View all available purchase options and get full access to this article.
References
Abdel-Kader, R. F. (2011). “Hybrid discrete PSO with GA operators for efficient QoS-multicast routing.” Ain Shams Eng. J., 2(1), 21–31.
Abrahart, R. J., Kneale, P. E., and See, L. M. (2004). Neural networks for hydrological modelling, Taylor and Francis, London, U.K.
Aqil, M., Kita, I., Yano, A., and Nishiyama, S. (2007). “A comparative study of artificial neural networks and neuro-fuzzy in continuous modeling of the daily and hourly behaviour of runoff.” J. Hydrol., 337(1–2), 22–34.
Chang, F. J., and Chen, Y. C. (2001). “A counterpropagation fuzzy-neural network modeling approach to real time streamflow prediction.” J. Hydrol., 245(1–4), 153–164.
Chau, K. (2004). “River stage forecasting with particle swarm optimization.” Proc., 17th Int. Conf. on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE 2004, Springer, Berlin, Heidelberg, Germany, 1166–1173.
Chau, K. W. (2006). “Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River.” J. Hydrol., 329(3–4), 363–367.
Chau, K. W., Wu, C. L., and Li, Y. S. (2005). “Comparison of several flood forecasting models in Yangtze River.” J. Hydrol. Eng., 485–491.
Cheng, C. T., Wang, W. C., Xu, D. M., and Chau, K. W. (2008). “Optimizing hydropower reservoir operation using hybrid genetic algorithm and chaos.” Water Resour. Manage., 22(7), 895–909.
Chua, L. H. C., Wong, T. S. W., and Sriramula, L. K. (2008). “Comparison between kinematic wave and artificial neural network models in event-based runoff simulation for an overland plane.” J. Hydrol., 357(3–4), 337–348.
Coulibaly, P., Anctil, F., Bobée, B. (2000). “Daily reservoir inflow forecasting using artificial neural networks with stopped training approach.” J. Hydrol., 230(3–4), 244–257.
De Vos, N. J., and Rientjes, T. H. M. (2008). “Multiobjective training of artificial neural networks for rainfall-runoff modeling.” Water Resour. Res., 44(8), 1–15.
Eberhart, R., and Kennedy, J. (1995). “New optimizer using particle swarm theory.” Proc., 6th Int. Symp. on Micro Machine and Human Science, IEEE, IEEE, New York, 39–43.
Eberhart, R. C., and Shi, Y. (2000). “Comparing inertia weights and constriction factors in particle swarm optimization.” Proc., 2000 Congress on Evolutionary Computation CEC 00, IEEE, IEEE, New York, 84–88.
El-Gallad, A., El-Hawary, M., Sallam, A., and Kalas, A. (2002). “Enhancing the particle swarm optimizer via proper parameters selection.” Proc., 2002 IEEE Canadian Conf. on Electrical and Computer Engineering, IEEE, New York, 792–797.
Elshorbagy, A., Simonovic, S. P., and Panu, U. S. (2000). “Performance evaluation of artificial neural networks for runoff prediction.” J. Hydrol. Eng., 424–427.
Farahnakian, M., Razfar, M. R., Moghri, M., and Asadnia, M. (2011). “The selection of milling parameters by the PSO-based neural network modeling method.” Int. J. Adv. Manuf. Technol., 57(1–4), 49–60.
Firat, M., and Güngör, M. (2008). “Hydrological time-series modelling using an adaptive neuro-fuzzy inference system.” Hydrol. Process., 22(13), 2122–2132.
Furundzic, D. (1998). “Application example of neural networks for time series analysis: Rainfall-runoff modeling.” Signal Process., 64(3), 383–396.
Gupta, H. V., Hsu, K.-L., and Sorooshian, S. (1997). “Superior training of artificial neural networks using weight-space partitioning.” Proc., 1997 IEEE Int. Conf. on Neural Networks, Part 4 (of 4), IEEE, Houston, TX, 1919–1923.
Hamilton, W. D. (1971). “Geometry for the selfish herd.” J. Theor. Biol., 31(2), 295–311.
He, S., Wu, Q. H., Wen, J. Y., Saunders, J. R., and Paton, R. C. (2004). “A particle swarm optimizer with passive congregation.” BioSystems, 78(1–3), 135–147.
Hsu, K.-L., Gupta, H. V., and Sorooshian, S. (1995). “Artificial neural network modeling of the rainfall-runoff process.” Water Resour. Res., 31(10), 2517–2530.
Jain, A., and Prasad Indurthy, S. K. V. (2003). “Comparative analysis of event-based rainfall-runoff modeling techniques—Deterministic, statistical, and artificial neural networks.” J. Hydrol. Eng., 93–98.
Kuo, C. C., Gan, T. Y., and Yu, P. S. (2010). “Seasonal streamflow prediction by a combined climate-hydrologic system for river basins of Taiwan.” J. Hydrol., 387(3–4), 292–303.
Kurtulus, B., and Razack, M. (2007). “Evaluation of the ability of an artificial neural network model to simulate the input-output responses of a large karstic aquifer: The La Rochefoucauld aquifer (Charente, France).” Hydrogeol. J., 15(2), 241–254.
Li, L., and Chu, X. (2011). “An improved particle swarm optimization algorithm with harmony strategy for the location of critical slip surface of slopes.” China Ocean Eng., 25(2), 357–364.
Lin, J. Y., Cheng, C. T., and Chau, K. W. (2006). “Using support vector machines for long-term discharge prediction.” Hydrol. Sci. J., 51(4), 599–612.
Liu, B., Wang, L., Jin, Y. H., Tang, F., and Huang, D. X. (2005). “Improved particle swarm optimization combined with chaos.” Chaos, Solitons Fractals, 25(5), 1261–1271.
Maier, H. R., and Dandy, G. C. (1997). “Determining inputs for neural network models of multivariate time series.” Microcomput. Civ. Eng., 12(5), 353–368.
Marquardt, D. W. (1963). “An algorithm for least-squares estimation of nonlinear parameters.” J. Soc. Ind. Appl. Math., 11(2), 431–441.
Mukerji, A., Chatterjee, C., and Singh Raghuwanshi, N. (2009). “Flood forecasting using ANN, neuro-fuzzy, and neuro-GA models.” J. Hydrol. Eng., 647–652.
Nayak, P. C., Sudheer, K. P., and Jain, S. K. (2007). “Rainfall-runoff modeling through hybrid intelligent system.” Water Resour. Res., 43(7), 1–17.
Nayak, P. C., Sudheer, K. P., Rangan, D. M., and Ramasastri, K. S. (2005). “Short-term flood forecasting with a neurofuzzy model.” Water Resour. Res., 41(4), 1–16.
NeuroDimension. (2005). NeuroSolutions version 5.02, Gainesville, FL.
Panda, S., and Padhy, N. P. (2008). “Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design.” Appl. Soft Comput., 8(4), 1418–1427.
Qiao, L., Peng, X., and Peng, Y. (2006). “An improvement on particle swarm optimization.” Chin. J. Electron., 15(2), 261–264.
Raghuwanshi, N. S., Singh, R., and Reddy, L. S. (2006). “Runoff and sediment yield modeling using artificial neural networks: Upper Siwane River, India.” J. Hydrol. Eng., 71–79.
Razfar, M. R., Asadnia, M., Haghshenas, M., and Farahnakian, M. (2010). “Optimum surface roughness prediction in face milling X20Cr13 using particle swarm optimization algorithm.” Proc. IME. B J. Eng. Manufact., 224(11), 1645–1653.
Senthil Kumar, A. R., Ojha, C. S. P., Goyal, M. K., Singh, R. D., and Swamee, P. K. (2012). “Modeling of suspended sediment concentration at Kasol in India using ANN, fuzzy logic, and decision tree algorithms.” J. Hydrol. Eng., 394–404.
Shi, Y., and Eberhart, R. (1998a). “Modified particle swarm optimizer.” Proc., 1998 IEEE Int. Conf. on Evolutionary Computation, ICEC’98, IEEE, IEEE Neural Networks Council, New York, 69–73.
Shi, Y., and Eberhart, R. (1998b). “Parameter selection in particle swarm optimization.” Proc., Evolutionary Programming VII, 7th International Conference, EP98, Springer, Berlin, Heidelberg, Germany.
Sudheer, K. P., Gosain, A. K., and Ramasastri, K. S. (2002). “A data-driven algorithm for constructing artificial neural network rainfall-runoff models.” Hydrol. Process., 16(6), 1325–1330.
Talei, A., and Chua, L. H. C. (2012). “Influence of lag time on event-based rainfall–runoff modeling using the data driven approach.” J. Hydrol., 438–439, 223–233.
Talei, A., Chua, L. H. C., and Quek, C. (2010). “A novel application of a neuro-fuzzy computational technique in event-based rainfall-runoff modeling.” Expert Syst. Appl., 37(12), 7456–7468.
Talei, A., Chua, L. H. C., Quek, C., and Jansson, P.-E. (2013). “Runoff forecasting using a Takagi–Sugeno neuro-fuzzy model with online learning.” J. Hydrol., 488, 17–32.
Taormina, R., Chau, K. W., and Sethi, R. (2012). “Artificial neural network simulation of hourly groundwater levels in a coastal aquifer system of the Venice lagoon.” Eng. Appl. Artif. Intell., 25(8), 1670–1676.
Tayfur, G., and Singh, V. P. (2006). “ANN and fuzzy logic models for simulating event-based rainfall-runoff.” J. Hydraul. Eng., 1321–1330.
Tokar, A. S., and Johnson, P. A. (1999). “Rainfall-runoff modeling using artificial neural networks.” J. Hydrol. Eng., 232–239.
Wang, W. C., Xu, D. M., Cheng, C. T., and Chau, K. W. (2012). “Calibration of Xinanjiang model parameters using hybrid genetic algorithm based fuzzy optimal model.” J. Hydroinf., 14(3), 784–799.
Wu, C. L., Chau, K. W., and Li, Y. S. (2009). “Predicting monthly streamflow using data-driven models coupled with data-preprocessing techniques.” Water Resour. Res., 45(8), 1–23.
Wu, J. S., Han, J., Annambhotla, S., and Bryant, S. (2005). “Artificial neural networks for forecasting watershed runoff and stream flows.” J. Hydrol. Eng., 216–222.
Yu, J., and Guo, P. (2012). “Improved PSO algorithm with harmony search for complicated function optimization problems.” 9th Int. Symp. on Neural Networks, Springer, Berlin, Heidelberg, Germany, 624–632.
Yu, J., Qin, X., Larsen, O., and Chua, L. (2014). “Comparison between response surface models and artificial neural networks in hydrologic forecasting.” J. Hydrol. Eng., 473–481.
Yu, J., Wang, S., and Xi, L. (2008). “Evolving artificial neural networks using an improved PSO and DPSO.” Neurocomputing, 71(4–6), 1054–1060.
Zhang, J., Liu, K., and Tan, Y. (2010). “KNOB particle swarm optimizer.” Proc., 1st Int. Conf. on Advances in Swarm Intelligence, ICSI 2010, IEEE, New York, 78–85.
Information & Authors
Information
Published In
Copyright
© 2013 American Society of Civil Engineers.
History
Received: Jan 28, 2013
Accepted: Oct 22, 2013
Published online: Oct 24, 2013
Discussion open until: Mar 24, 2014
Published in print: Jul 1, 2014
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.