Neural network river forecasting with multi-objective fully informed particle swarm optimization

Journal of Hydroinformatics

Volumn 17, Issue 1, 2015, Pages 99-113

  Taormina, Riccardo ^a   Chau, Kwok Wing ^a  

^a HONG KONG POLYTECHNIC UNIVERSITY   (Hong Kong)

Author keywords

FIPS; Multi objective; Neural network river forecasting; NNRF; Particle swarm optimization; PSO

Indexed keywords

EID: 84924153739     PISSN: 14647141     EISSN: None     Source Type: Journal    
DOI: 10.2166/hydro.2014.116     Document Type: Article

References (44)

1. Abrahart, R. J., Anctil, F., Coulibaly, P., Dawson, C. W., and Mount., N. J., See, L. M., Shamseldin, A. Y., and Solomatine., D. P., Toth, E. & Wilby, R. L. 2012 Two decades of anarchy? Emerging themes and outstanding challenges for neural network river forecasting. Prog. Phys. Geog. 36 (4), 418-513.
2. Adamowski, J. & Chan, H. F. 2011 A wavelet neural network conjunction model for groundwater level forecasting. J. Hydrol. 407 (1-4), 28-40.
3. Adamowski, J. & Karapataki, C. 2010 Comparison of multivariate regression and artificial neural networks for peak urban water-demand forecasting: evaluation of different ANN learning algorithms. J. Hydrol. Eng. 15 (10), 729-743.
4. Banerjee, P., and Singh., V. S., Chatttopadhyay, K., Chandra, P. C. & Singh, B. 2011 Artificial neural network model as a potential alternative for groundwater salinity forecasting. J. Hydrol. 398 (3-4), 212-220.
5. Carcano, E. C, Bartolini, P., Muselli, M. & Piroddi, L. 2008 Jordan recurrent neural network versus IHACRES in modelling daily streamflows. J. Hydrol. 362 (3-4), 291-307.
6. 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.
7. Chau, K. W. 2007 A split-step particle swarm optimization algorithm in river stage forecasting. J. Hydrol. 346 (3-4), 131-135.
8. Chen, Y. & Chang, F. 2009 Evolutionary artificial neural networks for hydrological systems forecasting. J. Hydrol. 367 (1-2), 125-137.
9. Clerc, M. & Kennedy, J. 2002 The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Trans. Evol. Comput. 6 (1), 58-73.
10. 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.
11. De Vos, N. J. & Rientjes, T. H. M. 2008 Multiobjective training of artificial neural networks for rainfall-runoff modeling. Water Resour. Res. 44 (8), doi: 10.1029/2007WR006734.
12. Deb, K. 2009 Multi-Objective Optimization Using Evolutionary Algorithms. John Wiley & Sons, New York.
13. Deb, K., Pratap, A., Agarwal, S. & Meyarivan, T 2002 A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6 (2), 182-197.
14. Eberhart, R & Kennedy, J. 1995 A new optimizer using particle swarm theory. Micro Machine and Human Science, 1995. MHS '95. In: Proceedings of the Sixth International Symposium, pp. 39-43.
15. Gill, M. K, Kaheil, Y H., Khalil, A., McKee, M. & Bastidas, L. 2006 Multiobjective particle swarm optimization for parameter estimation in hydrology. Water Resour. Res. 42 (7), doi: 10.1029/2005WR004528.
16. Guo, X., Hu, T, Zhang, T & Lv, Y. 2012 Bilevel model for multi-reservoir operating policy in inter-basin water transfer-supply project. J. Hydrol. 424-425 (6), 252-263.
17. Hamed, M. M., Khalafallah, M. G & Hassanien, E. A 2004 Prediction of wastewater treatment plant performance using artificial neural networks. Environ. Model. Softw. 19 (10), 919-928.
18. Islam, A. 2010 Improving flood forecasting in Bangladesh using an artificial neural network. J. Hydroinf. 12 (3), 351-364.
19. Jain, A. & Kumar, S. 2009 Dissection of trained neural network hydrologic models for knowledge extraction. Water Resour. Res. 45 (7), doi: 10.1029/2008WR007194.
20. Jain, A., Sudheer, K & Srinivasulu, S. 2004 Identification of physical processes inherent in artificial neural network rainfall runoff models. Hydrol. Process. 18 (3), 571-581.
21. Kennedy, J. 1999 Small worlds and mega-minds: effects of neighborhood topology on particle swarm performance. Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress 3, 1931-1938.
22. Kennedy, J. 2006 Swarm intelligence. In: Handbook of Nature-Inspired and Innovative Computing (A. Zomaya, ed.). Springer, USA, pp. 187-219.
23. Kennedy, J. & Mendes, R 2002 Population structure and particle swarm performance. Evolutionary Computation, 2002. CEC'02 Proceedings of the 2002 Congress 2, 1671-1676.
24. Knowles, J. & Corne, D. 1999 The pareto archived evolution strategy: A new baseline algorithm for pareto multiobjective optimisation. Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress 1, 5 pp.
25. Lin, G & Wu, M. 2009 A hybrid neural network model for typhoon-rainfall forecasting. J. Hydrol. 375 (3-4), 450-458.
26. Maier, H. R. & Dandy, G. C. 2000 Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environ. Model. Softw. 15 (1), 101-124.
27. Maier, H. R., Jain, A., Dandy, G. C. & Sudheer, K. P. 2010 Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environ. Model. Softw. 25 (8), 891-909.
28. Mendes, R, Kennedy, J. & Neves, J. 2003 Watch thy neighbor or how the swarm can learn from its environment. Swarm Intelligence Symposium, 2003. SIS'03. Proceedings of the 2003 IEEE 1, 88-94.
29. Mendes, R, Kennedy, J. & Neves, J. 2004 The fully informed particle swarm: simpler, maybe better. IEEE Trans. Evol. Comput. 8 (3), 204-210
30. Montalvo, I., Izquierdo, J., Pérez-García, R & Herrera, M. 2010 Improved performance of PSO with self-adaptive parameters for computing the optimal design of water supply systems. Eng. Appl. Artif. Intell. 23 (5), 727-735.
31. Nayak, P., Venkatesh, B., Krishna, B. & Jain, S. 2013 Rainfall runoff modelling using conceptual, data driven and wavelet based computing approach. J. Hydrol. 493 (17), 57-67.
32. Piotrowski, A. P. & Napiorkowski, J. J. 2011 Optimizing neural networks for river flow forecasting - evolutionary computation methods versus the Levenberg-Marquardt approach. J. Hydrol. 407 (1-4), 12-27.
33. Poli, R, Kennedy, J. & Blackwell, T. 2007 Particle swarm optimization. Swarm Intell. 1 (1), 33-57.
34. Reddy, M. J. & Kumar, D. N. 2009 Performance evaluation of elitist-mutated multi-objective particle swarm optimization for integrated water resources management. J. Hydroinf. 11 (1), 78-88.
35. Reyes-Sierra, M. & Coello Coello, C. A. 2006 Multi-Objective Particle Swarm Optimizers: A Survey of the State-of-the-Art. Technical Report EVOCINV-01-2006, Evolutionary Computation Group at CINVESTAV, Seccíon de Computacíon, Departamento de Ingeniería Eléctrica, CINVESTAV-IPN, Mexico.
36. Scheerlinck, K, Pauwels, V. R N, Vernieuwe, H. & De Baets, B. 2009 Calibration of a water and energy balance model: Recursive parameter estimation versus particle swarm optimization. Water Resour. Res. 45 (10), doi: 10.1029/2009WR008051.
37. Shamseldin, A. 2010 Artificial neural network model for river flow forecasting in a developing country. J. Hydroinform. 12 (1), 22-35.
38. Taormina, R, Chau, K & 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.
39. Tokar, A. S. & Markus, M. 2000 Precipitation-runoff modeling using artificial neural networks and conceptual models. J. Hydrol. Eng. 5 (2), 156-161.
40. Wilby, R, Abrahart, R. & Dawson, C. 2003 Detection of conceptual model rainfall-runoff processes inside an artificial neural network. Hydrol. Sci. J. 48 (2), 163-181.
41. Wu, C. L. & Chau, K W. 2011 Rainfall-runoff modeling using artificial neural network coupled with singular spectrum analysis. J. Hydrol. 399 (3-4), 394-409.
42. Wu, C. L., Chau, K W. & Fan, C. 2010 Prediction of rainfall time series using modular artificial neural networks coupled with data-preprocessing techniques. J. Hydrol. 389 (1-2), 146-167.
43. Zhang, X., Srinivasan, R, Zhao, K & Liew, M. V. 2009 Evaluation of global optimization algorithms for parameter calibration of a computationally intensive hydrologic model. Hydrol. Process. 23 (3), 430-441.
44. Zitzler, E. & Thiele, L. 1999 Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans. Evol. Comput. 3 (4), 257-271.