Reservoir Sedimentation Estimation Using Artificial Neural Network
Publication: Journal of Hydrologic Engineering
Volume 14, Issue 9
Abstract
Conventional methods and models available for estimation of reservoir sedimentation process differ greatly in terms of complexity, inputs, and other requirements. An artificial neural network (ANN) model was used to estimate the volume of sediment retained in a reservoir. Annual rainfall, annual inflow, and capacity of the reservoir were chosen as inputs. Thirty Two years of data pertaining to Gobindsagar Reservoir on the Satluj River in India, were used in this study (23 years for training and 9 years for testing). The pattern of the sediment volume retained in this reservoir was well captured by the Multi-Layer Perceptron (3–5-1) ANN model using the back propagation algorithm. Based on several performance indices, it was found that the ANN model estimated the volume of sediment retained in the reservoir with better accuracy and less effort as compared to conventional regression analysis.
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Acknowledgments
The writers thank Bhakra Beas Management Board (BBMB) for providing necessary data to carry out this work. Special thanks are owed to Professor M. C. Deo, Department of Civil Engineering, Indian Institute of Technology Bombay, India, for his support and valuable comments. They gratefully acknowledge the anonymous reviewers and editors for their valuable reviews and suggestions.
References
Abrahart, R. J., and White, S. M. (2001). “Modeling sediment transfer in Malawi: Comparing backpropagation neural network solutions against a multiple linear regression benchmark using small data sets.” Phys. Chem. Earth, Part B, 26(1), 19–24.
Agarwal, A., Singh, R. D., Mishra, S. K., and Bhunya, P. K. (2005). “ANN-based sediment yield models for Vamsadhara River basin (India).” Water SA, 31(1), 95–100.
ASCE Task Committee on Application of the Artificial Neural Networks in Hydrology. (2000a). “Artificial neural networks in hydrology I: Preliminary concepts.” J. Hydrol. Eng., 5(2), 115–123.
ASCE Task Committee on Application of the Artificial Neural Networks in Hydrology. (2000b). “Artificial neural networks in hydrology II: Hydrologic applications.” J. Hydrol. Eng., 5(2), 124–137.
Bhattacharya, B., Price, R. K., and Solomatine, D. P. (2005). “Data-driven modelling in the context of sediment transport.” Physics and Chemistry of the Earth, 30, 297–302.
Brown, C. B. (1943). “Discussion of sedimentation in reservoirs.” Trans. Am. Soc. Civ. Eng., 109, 1080–1086.
Brune, G. M. (1953). “Trap efficiency of reservoirs.” Trans., Am. Geophys. Union, 34(3), 407–418.
Central Water Commission (CWC). (2001). Compendium on silting of reservoirs in India, Water Planning and Projects Wing, Environment Management Organisation, Watershed and Reservoir Sedimentation Directorate, CWC, New Delhi, India.
Cigizoglu, H. K. (2002a). “Suspended sediment estimation and forecasting using artificial neural networks.” Turk. J. Eng. Environ. Sci., 26, 15–25.
Cigizoglu, H. K. (2002b). “Suspended sediment estimation for rivers using artificial neural networks and sediment rating curves.” Turk. J. Eng. Environ. Sci., 26, 27–36.
Cigizoglu, H. K. (2004). “Estimation and forecasting of daily suspended sediment data by multi-layer preceptrons.” Adv. Water Resour., 27, 185–195.
Cigizoglu, H. K., and Alp, M. (2006). “Generalized regression neural network in modeling river sediment yield.” Adv. Eng. Software, 37, 63–68.
Das, S. K., and Basudhar, P. K. (2006). “Undrained lateral load capacity of piles in clay using artificial neural network.” Comp. Geotechn., 33, 454–459.
Durbude, G. D., and Purandara, B. K. (2005). “Assessment of sedimentation in the Linganmakki Reservoir using remote sensing.” J. Indian Society of Remote Sensing, 33(4), 503–510.
Garson, G. D. (1991). “Interpreting neural-network connection weights.” AI Expert, 6(4), 47–51.
Goh, A. T. C. (1994). “Seismic liquefaction potential assessed by neural networks.” J. Geotech. Engrg., 120(9), 1467–1480.
Hammerstrom, D. (1993). “Working with neural networks.” IEEE Spectrum, 30(7), 46–53.
Haykin, S. (1999). Neural network: A comprehensive foundation, Prentice-Hall, Upper Saddle River, N.J.
Jothiprakash, V., and Garg, V. (2008). “Re-look to conventional techniques for trapping efficiency estimation of a reservoir.” Int. J. Sediment Res., 23(1), 76–84.
Lee, H-Y., Lin, Y-T. and Chiu, Y-J. (2006). “Quantitative estimation of reservoir sedimentation from three typhoon events.” J. Hydrol. Eng., 11(4), 362–370.
Licznar, P., and Nearing, M. A. (2003). “Artificial neural networks of soil erosion and runoff prediction at the plot scale.” Catena, 51, 89–114.
Mahmood, K. (1987). Reservoir sedimentation: Impact, Extent, Mitigation, Technical Rep. No. 71, World Bank, Washington, D.C.
Morris, G. L., and Fan, J. (1998). Reservoir sedimentation handbook, McGraw-Hill, New York.
Nash, J. E., and Sutcliffe, J. V. (1970). “River flow forecasting through conceptual models. 1: A discussion of principles.” J. Hydrol., 10, 282–290.
Olden, J. D., Joy, M. K., and Death, R. G. (2004). “An accurate comparison of methods for quantifying variable importance in artificial neural networks using simulated data.” Ecol. Modell., 178, 389–397.
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., 11(1), 71–79.
Sarangi, A., and Bhattacharya, A. K. (2005). “Comparison of artificial neural network and regression models for sediment loss prediction from Banha Watershed in India.” Agric. Water Manage., 78, 195–208.
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.” Curr. Sci., 89(12), 2022–2033.
Shangle, A. K. (1991). “Reservoir sedimentation status in India.” Jalvigyan Sameeksha, 5, 63–70.
Sharma, P. D., Goel, A. K., and Minhas, R. S. (1991). “Water and sediment yields into the Satluj River from the High Himalaya.” Mountain Res. Devel., 11(2), 87–100.
Srinivasulu, S., and Jain, A. (2006). “A comparative analysis of training methods for artificial neural network rainfall runoff models.” Appl. Soft Comput., 6, 295–306.
Yang, X. (2003). Manual on Sediment Management and Measurement, World Meteorological Organization, Operational Hydrology Rep. No. 47, WMO-No. 948, Secretariat of the World Meteorological Organization, Geneva, Switzerland.
Yitian, L., and Gu, R. R. (2003). “Modeling flow and sediment transport in a river system using an artificial neural network.” Environ. Manage., 31(1), 122–134.
Yoon, Y. N. (1992). “The state and the perspective of the direct sediment removal methods from reservoirs.” Int. J. Sediment Res., 7(20), 99–115.
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© 2009 ASCE.
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Received: Dec 9, 2007
Accepted: Jan 29, 2009
Published online: Aug 14, 2009
Published in print: Sep 2009
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