Geomorphology-based Time-Lagged Recurrent Neural Networks for runoff forecasting
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  • 作者：Manabendra Saharia (1) msaharia@live.com
    Rajib Kumar Bhattacharjya (2) rkbc@iitg.ernet.in
  • 关键词：rainfall ; runoff modelling – ; artificial neural networks – ; Time ; Lagged Recurrent Neural Network (TLRN) – ; gamma memory
  • 刊名：KSCE Journal of Civil Engineering
  • 出版年：2012
  • 出版时间：July 2012
  • 年：2012
  • 卷：16
  • 期：5
  • 页码：862-869
  • 全文大小：591.2 KB
  • 参考文献：1. Alvisi, S., Mascellani, G., Franchini, M., and Bardossy, A. (2006). “Water level forecasting through fuzzy logic and artificial neural network approaches.” Hydrology and Earth System Sciences, Vol. 10, No. 1, pp. 1–17.
    2. Anmala, J., Zhang, B., and Govindaraju, R. (2000). “Comparison of ANNsand empirical approaches for predicting watershed runoff.” ASCE Journal of Water Resources Planning and Management, Vol. 126, No. 3, pp. 156–166.
    3. ASCE Task Committee (2000). “Articial neural networks in hydrology, 1 & 2. Hydrologic applications.” Journal of Hydrologic Engineering, ASCE, Vol. 5, No. 2, pp. 124–137.
    4. Badjate, S. L. and Dudul, S. V. (2009). “Multi step ahead prediction of north and south hemisphere sun spots chaotic time series using focused time lagged recurrent neural network model.” WSEAS Trans. Infor. Sci. Appl., Vol. 6, No. 4, pp. 684–693.
    5. Chau, K. W., Wu, C. L., and Li, Y. S. (2005). “Comparison of several flood forecasting models in Yangtze River.” Journal of Hydrologic Engineering, ASCE, Vol. 10, No. 6, pp. 485–491.
    6. Chen, J. and Adams, B. J. (2006). “A framework for urban storm water modeling andcontrol analysis with analytical models.” Water Resources Research, Vol. 42, W06419, 13 PP., doi:10.1029/2005WR004540.
    7. Chow, T. W. S. and Cho, S. Y. (1997). “Development of a recurrent sigma-pi neural network rainfall forecasting system in Hong Kong.” Neural Computing and Applications, Vol. 5, No. 2, pp. 66–75.
    8. Coulibaly, P., Anctil, F., and Bobe茅, B. (2000) “Daily reservoir inflow forecasting using artificial neural networks with stopped training approach.” Journal of Hydrologic Engineering, ASCE, Vol. 230,Issues 3–4, pp. 244–257.
    9. Coulibaly, P., Anctil, F., and Bobe茅, B. (2001) “Multivariate reservoir inflow forecasting using temporal neural networks.” Journal of Hydrologic Engineering, ASCE, Vol. 6, No. 5, pp. 367–376.
    10. De Vries, B. and Principe, J. C. (1992). “The gamma model — A new neural model for temporal processing,” Neural Networks, Vol. 5,Issue 4, pp. 565–576.
    11. Dibike, Y. B., Solomatine, D., and Abbott, M. B. (1999). “On theencapsulation of numerical-hydraulic models in artificialneural network.” Journal of Hydraulic Research, Vol. 37, No. 2, pp. 147–161.
    12. Fausett, L. (1994). Fundamentals of neural networks, Prentice Hall, Englewood Cliffs, N.J.
    13. Gautam, M. R., Watanbe, K., and Saegusa, H. (2000). “Runoff analysis in humid forest catchment with artificial neural network.” Journal of Hydrology, Vol. 235, Nos. 1–2, pp. 117–136.
    14. Govindaraju, R. S. and Rao, A. R. (2000). “Introduction.” Articial Neural Networks in Hydrology, In: Govindararaju, R. S. and Rao, A. R. (eds.), Kluwer, Dordrecht, Netherlands, pp. 1–7.
    15. Gupta, H. V., Hsu, K., and Sorooshian, S. (2000). “Effective and efcient modeling for streamow forecasting.” Articial Neural Networks in Hydrology, In: Govindaraju, R. S. and Rao, A. R. (eds.), Kluwer, Dordrecht, pp. 7–22.
    16. Haykin, S. (1999). Neural networks: A comprehensive foundation, MacMillan, New York.
    17. Horton, R. E. (1945). “Erosional development of streams and their drainage basins: Hydrophysical approach to quantitative morphology.” Bull. Geol. Soc. Am., Vol. 56, No. 3, pp. 275–370.
    18. Hsu, K., Gao, X., and Sorooshain, S. (1997). “Precipitation estimation from remotely sensed information using articial neural network.” Journal of Applied Meteorology, Vol. 36, No. 9, pp. 1176–1190.
    19. Huang, W., Murray, C., Kraus, N., and Rosati, J. (2003). “Development of a regional artificial neural network prediction for coastal water level prediction.” Ocean Engineering, Vol. 30,Issue 17, pp. 2275–2295.
    20. Hussain, A. J., Ghazali, R., and Jumeily, D. A. (2008). “Dynamic Ridge Polynomial Neural Networks for multi-step financial time-series prediction.” Int. J. Intell. Syst. Technol. Appl., Vol. 5, No. 1/2, pp. 45–165.
    21. Jain, S. K., Das, D., and Srivastava, D. K. (1999). “Application of ANN for reservoir inflow prediction and operation.” J Water Resour Plng Mgmt, ASCE, Vol. 125, No. 5, pp. 263–271.
    22. Kale, S. N. and Dudul, S. V. (2009). “Intelligent noise removal from EMG signal using focused time-lagged recurrent neural network.” Applied Computational Intelligence and Soft Computing, Article ID129761, doi:10.1155/2009/129761, p. 12.
    23. Kişi, 脰. (2007). “Streamflow forecasting using different artificial neural network algorithms.” Journal of Hydrologic Engineering, ASCE, Vol. 12, No. 5, pp. 532–539.
    24. Kote, A. S. and Jothiprakash, V. (2008). “Reservoir inflow prediction using time lagged recurrent neural networks.” IEEE Computer Society, pp. 618–623.
    25. Legates, D. R. and McCabe Jr., G. J. (1999). “Evaluating the use of “goodness-of-fit” Measures in hydrologic and hydroclimatic model validation.” Water Resources Research, Vol. 35, No. 1, pp. 233–241, doi:10.1029/1998WR900018.
    26. Maier, H. R. and Dandy, G. C. (2000). “Neural networks for the prediction and forecasting of water resources variables: A review of modelling Issues and applications.” Environmental Modelling & Software, Vol. 15,Issue 1, pp. 101–124.
    27. Mendel, J. M. and McLaren, R. W. (1970). “Reinforcement learning control andpattern recognition systems.” Adaptive, Learning and Pattern Recognition Systems: Theory and Applications, In Mendel, J. M. and Fu, K. S. (eds.), pp. 287–318.
    28. Motter, M. and Principe, J. (1994). “A gamma memory neural network for system identification.” In Proc. IEEE WCCI’ 94, Orlando, FL, Vol. 5, pp. 3232–3237.
    29. Narendra, K. S. (1991). “Adaptive control using neural networks.” Neural Networks for Control, W. Thomas Miller, Richard S. Sutton, and Paul J. Werbos, Editors. Cambridge, MA: The MIT Press.
    30. Nash, J. E. and Sutcliffe, J. V. (1970). “River flow forecasting through conceptual models, Part-1. A discussion of principles.” Journal of Hydrology,Vol. 10,Issue 3, pp. 282–290.
    31. Rodr铆guez-Iturbe, I. and Vald茅s, J. B. (1979). “The geomorphologic structure of hydrologic response.”Water Resources Research, Vol. 15, No. 6, pp. 1409–1420, doi:10.1029/WR015i006p01409.
    32. Sarangi, A., Madramootoo, C. A., Enright, P., Prasher, S. O., and Patel, R. M. (2005). “Performance evaluation of ANN and geomorphology-based models for runoff and sediment yield prediction for a Canadian watershed.” Current Science, Vol. 89, No. 12, pp. 2022–2033.
    33. Shahin, M. A., Maier, H. R., and Jaksa, M. B. (2002). “Predicting settlement of shallow foundations using neural networks.” Journal of Geotechnical and Geoenviromental Engineering, ASCE, Vol. 128, No. 9, pp. 785–793.
    34. Strahler, A. N. (1957) “Quantitative analysis of watershed geomorphology.” Transaction of American Geophysical Union, Vol. 38, No.6, pp. 913–920.
    35. Thirumalaiah, K. and Deo, M. C. (2000). “Hydrological forecasting using artificial neural network.” Journal of Hydrologic Engineering, ASCE, Vol. 5, No. 2, pp. 180–189.
    36. Tokar, A. S. and Johnson, P. A. (1999). “Rainfall-runoff modeling using artificial neural networks.” Journal of Hydrologic Engineering, ASCE, Vol. 4, No. 3, pp. 232–239.
    37. Wang, Y. M. and Traore, S. (2009). “Time-lagged recurrent network for forecasting episodic event suspended sediment load in typhoon prone area.” International Journal of Physical Sciences, Vol. 4, No.9, pp. 519–528.
    38. Wright, N. G., Dastorani, M. T., Goodwin, P., and Slaughter, C. W., (2002). “A combination of neural networks and hydrodynamic models for river flow prediction.” Proceedings of the Fifth International Conference on Hydroinformatics, Cardiff, U.K.
    39. Williams, R. J. and Peng, J. (1990). “An efficient gradient-based algorithm for online training of recurrent network trajectories.” Neural Computation, Vol. 2, No. 4, pp. 490–501.
    40. Xue, Y. and Dibike, Y. B. (2001). “Flood forecasting mode for Huai River in China using time delay neural network.” Proceedings of the XXIX IAHR congress, Beijing, China, Theme C, pp. 59–66.
    41. Yapo, P. O., Gupta, H. V., and Sorooshian, S. (1998). “Multi-objective global optimization for hydrologic models.” Journal of Hydrology, Vol. 204,Issues 1–4, pp. 83–97.
    42. Yitian, L. I. and Gu, R. R. (2003). “Modeling flow and sediment transport in a river system using an artificial neural network.” Environmental Management, Vol. 31, No. 1, pp. 122–134.
    43. Zealand, C. M., Burn, D. H., and Simonovic, S. P. (1999). “Short term streamflow forecasting using artificial neural networks.” Journal of Hydrology, Vol. 214,Issues 1–4, pp. 32–48.
    44. Zhang, B. and Govindaraju, R. S. (2000). “Prediction of watershed runoff using Bayesian concepts and modular neural networks.” Water Resources Research, Vol. 36, No. 3, pp. 753–762.
    45. Zhang, B. and Govindaraju, R. S. (2003). “Geomorphology-based Artificial Neural Networks (GANNs) for estimation of direct runoff over watersheds.” Journal of Hydrology, Vol. 273,Issues 1–4, pp. 18–34.
  • 作者单位：1. Dept. of Civil Engineering, National Institute of Technology, Silchar, India, 788010 Assam, India2. Dept. of Civil Engineering, Indian Institute of Technology, Guwahati, 781039 Assam, India
  • ISSN：1976-3808
文摘
Artificial Neural Networks have been widely used to develop effective runoff-forecasting models. An overwhelming majority of networks are static in nature and also developed without incorporating geomorphologic information of the watershed. The objective of this study is to develop an efficient dynamic neural network model which also accounts for morphometric characteristics of the catchment. The model developed using Time-Lagged Recurrent Neural Networks (TLRNs) is used to estimate runoff for river Dikrong, a tributary of river Brahmaputra in India. Comparisons with traditional static models, with and without integration of geomorphologic data, reveal the proposed model to be a promising tool in operational hydrology.

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