The week's pick
Random Articles
Reseach Article
A Wavelet-ANFIS Model to Estimate Sedimentation in Dam Reservoir
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 114 - Number 9 |
| Year of Publication: 2015 |
| Authors: Mohamad Javad Alizdeh, Pejman Mohammadnia Joneyd, Meysam Motahhari, Farid Ejlali, Hamed Kiani |
10.5120/20007-1958
|
Mohamad Javad Alizdeh, Pejman Mohammadnia Joneyd, Meysam Motahhari, Farid Ejlali, Hamed Kiani . A Wavelet-ANFIS Model to Estimate Sedimentation in Dam Reservoir. International Journal of Computer Applications. 114, 9 ( March 2015), 19-25. DOI=10.5120/20007-1958
Abstract
An accurate prediction of sedimentation in dam reservoir is a challenging issue due to the complex and non-linear physics of the problem. Anyhow, soft-computing-based techniques showed great ability for predicting non-linear phenomena and have been used for different purposes. The main objective of this study is to estimate the volume of sedimentation in Karaj dam using a wavelet-ANFIS (WANFIS) and a wavelet-neural network (WANN) model. Monthly average flow is used to estimate monthly averaged suspended sediment load for a thirty-year period. The amount of bed load is computed based on the suspended sediment load and the river slope and the total volume of sedimentation in the reservoir is calculated with subtracting the upstream (Karaj River) and downstream (Beylaghan River) total sediment load. In WANFIS and WANN models, monthly average flow time series are decomposed to several sub-time series using different wavelet decomposition levels. The total volume of sedimentation in Karaj dam obtained from different techniques such as WANFIS, WANN, ANFIS, ANN and hydrography are compared together. The comparison demonstrates that WANFIS model is superior to the other techniques. For WANFIS and WANN models, the best model is obtained by two and three wavelet decomposition levels respectively. Findings of this study reveal that Wavelet-ANFIS models can be applied as a successful tool to predict the volume of sedimentation.
References
- http://britishdams. org/about_dams/sedimentation. htm
- http://www. internationalrivers. org/sedimentation-problems-with-dams
- Mishra, S. , Gupta, P. , Pandey, S. K and Shukla, J. P. 2014. An Efficient Approach of Artificial Neural Network in Runoff Forecasting. International Journal of Computer Applications (April 2014), 92(5):9-15.
- Saberi Nasr, A. , Rezaei, M and Dashti Barmaki, M. 2012. Analysis of Groundwater Quality using Mamdani Fuzzy Inference System (MFIS) in Yazd province, Iran. International Journal of Computer Applications (December 2012), 59(7):45-53.
- Nourani, V. 2009. Using ANNs for sediment forecasting of Talkherood river mouth. Journal of Urban and Environmental Engineering, 3(1), 1-6.
- Rajaee, T. 2010. Wavelet and Neuro-fuzzy Conjunction Approach for Suspended Sediment Prediction. Clean – Soil, Air, Water, 38 (3), 275 –288.
- Alizadeh, M. J. , Kavianpour, M. R. , Tahershamsi, A and Shahheydari, H. 2015. A Wavelet-ANN approach to investigate the effect of seasonal decomposition of time series in daily flow forecasting. In Proceeding of the 10th International Congress on Civil Engineering (10icce), University of Tabriz, Tabriz, Iran.
- Mirbagheri, S. A. , Nourani, V. , Rajaee, T and Alikhani, A. 2010. Neuro-fuzzy models employing wavelet analysis for suspended sediment concentration prediction in rivers. Hydrological Sciences Journal, 55(7), 1175-1189.
- Adib, A and Jahanbakhshan, H. 2013. Stochastic approach to determination of suspended sediment concentration in tidal rivers by artificial neural network and genetic algorithm. Canadian Journal of Civil Engineering, 40(4), 299-312.
- http://reference. wolfram. com/applications/neuralnetworks/NeuralNetworkTheory/2. 5. 1. html
- Sen, Z. 2001. Fuzzy logic and foundation. ISBN 9758509233, BilgeK¨ult¨urSanat, Publisher, Istabul, p. 172.
- Labat, D. , Ababou, R and Mangin, A. 2000. Rainfall–runoff relation for karstic spring. Part 2: continuous wavelet and discrete orthogonal multi resolution analyses. Journal of Hydrology, 238, 149–78.
- Cohen, A and Kovacevic, J. 1996. Wavelets: the mathematical background. In Proceedings of IEEE, 84(4), 514–522.
- Gupta, K. K and Gupta, R. 2007. Despeckle and geographical feature extraction in SAR images by wavelet transform. ISPRS J Photogramm, 62(6), 473–84.
- Ministry of Power. 1991. A Report of Sedimentation and Sediment Measurements of Amirkabir Dam, Institute of investigations and laboratories of Tehran water resources, p. 113, (in Persian).
- Mosaedi, S. F. , Hashemi Najafi, M. , Heydarnezhad, M and Meshkati, M. E. 2009. Estimation of sediment volumes in Karaj and Dez Reservoirs and their comparison with Hydrographic surveying. J. Agric. Sci. Natur. Resour, 16(Special issue 2), 261-272 (in Persian).
- Bahadori, F. 2000. Principles of river sand harvesting. Office of river engineering, coastal and flood control of Ministry of Energy, Tehran, p36 (in Persian).
Index Terms
Keywords