Comparison of Three Intelligent Techniques for Runoff Simulation
Abstract
In this study, performance of a feedback neural network, Elman, is evaluated for runoff simulation. The model ability is compared with two other intelligent models namely, standalone feedforward Multi-layer Perceptron (MLP) neural network model and hybrid Adaptive Neuro-Fuzzy Inference System (ANFIS) model. In this case, daily runoff data during monsoon period in a catchment located at south India were collected. Three statistical criteria, correlation coefficient, coefficient of efficiency and the difference of slope of a best-fit line from observed-estimated scatter plots to 1:1 line, were applied for comparing the performances of the models. The results showed that ANFIS technique provided significant improvement as compared to Elman and MLP models. ANFIS could be an efficient alternative to artificial neural networks, a computationally intensive method, for runoff predictions providing at least comparable accuracy. Comparing two neural networks indicated that, unexpectedly, Elman technique has high ability than MLP, which is a powerful model in simulation of hydrological processes, in runoff modeling.
Keywords
References
Rajurkar M. P., Kothyari U. C., Chaube U. C. "Modeling of the daily rainfall-runoff relationship with artificial neural network." Journal of Hydrology 285 (January 2004): 96-113. https://doi.org/10.1016/j.jhydrol.2003.08.011.
Agarwal A., Mishra S. K., Ram S., Singh J. K. "Simulation of Runoff and Sediment yield using Artificial Neural Networks.". Biosystems Engineering 94(2006): 597-613. https://doi.org/10.1016/j.biosystemseng.2006.02.014.
Raghuwanshi N. S., Singh R., Reddy L. S. "Runoff and Sediment Yield Modeling Using Artificial Neural Networks: Upper Siwane River, India." Journal of Hydrologic Engineering 11 (January 2006). doi: 10.1061/(ASCE)1084-0699(2006)11:1(71).
McCulloch W. S., Pitts W. "A logical calculus of the ideas immanent in nervous activity." Bulletin of Mathematical Biophysics 5(December 1943):115-133.
Smith J., Eli R. N. "Neural-network models of rainfall–runoff process." Journal of Water Resources Planning and Management 121(October 1995): 499–508. https://doi.org/10.1029/95WR01955.
Hsu K., Gupta H. V., Sorooshian S. "Artificial neural network modeling of the rainfall–runoff process." Water Resources Research 31(October 1995): 2517–2530. https://doi.org/10.1029/95WR01955.
Minns A. W., Hall M. J. "Artificial neural networks as rainfall–runoff models." Journal of Hydrologic Sciences 41 (January 1996): 399–417. https://doi.org/10.1080/02626669609491511.
Shamseldin A. Y. "Application of a neural network technique to rainfall–runoff modelling." Journal of Hydrology 199(December 1997): 272–294. https://doi.org/10.1016/S0022-1694(96)03330-6.
Dawson C. W., Wilby R. "An artificial neural network approach to rainfall–runoff modelling." Journal of Hydrologic Sciences 43(1998):47–66. https://doi.org/10.1080/02626669809492102.
Tokar A. S., Johnson P. A. "Rainfall–runoff modeling using artificial neural networks." Journal of Hydrologic Engineering 4 (Jully 1999): 232–239. https://doi.org/10.1061/(ASCE)1084-0699(1999)4:3(232).
Chiang Y. M., Chang L. C., Chang J. F. "Comparison of static-feed forward and dynamic-feedback neural networks for rainfall–runoff modelling". Journal of Hydrology 290 (May 2004): 297–311. https://doi.org/10.1016/j.jhydrol.2003.12.033.
Tayfur G., Singh V.P. "ANN and fuzzy logic models for simulating event-based rainfall–runoff." Journal of Hydrologic Engineering 132(December 2006):1321–1330. https://doi.org/10.1061/(ASCE)0733-9429(2006)132:12(1321).
Tayfur G., Moramorco T., Singh V. P. "Predicting and forecasting flow discharge at sites receiving signifi-cant lateral inflow." Journal of Hydrologic Processes 21(January 2007):1848–1859. https://doi.org/10.1002/hyp.6320.
Abudu S., Cui C.L., King J.P., Abudukadeer K. "Comparison of performance of statistical models in forecasting monthly streamflow of Kizil River, China." Water Science and Engineering 3(2010): 269-281. doi:10.3882/j.issn.1674-2370.2010.03.003.
Tampelini L.G., Boscarioli C., Peres S.M., Sampaio S.C. "An application of Elman networks in treatment and prediction of hydrologic time series." Learning and Nonlinear Models (L&NLM) – Journal of the Brazilian Neural Network Society 9(3) (January 2011): 148-156. doi: 10.21528/LNLM-vol9-no3-art1.
Sarkar A., Kumar R. "Artificial Neural Networks for Event Based Rainfall-Runoff Modeling." Journal of Water Resource and Protection 4(2012): 891-897. http://dx.doi.org/10.4236/jwarp.
Hasanpour K. M., Ghorbani M. A., Dinpashoh Y., Shahmorad S. "Comparison of Volterra Model and Artificial Neural Networks for Rainfall–Runoff Simulation." Natural Resources Research 23 (2014): doi: 10.1007/s11053-014-9235-y.
Devi S.R., Arulmozhivarman P., Venkatesh C., Agarwal P. "Performance comparison of artificial neural network models for daily rainfall prediction." International Journal of Automation and computing 13(October 2016): 417-427. doi: 10.1007/s11633-016-0986-2.
Jang J.R. “Anfis: adaptive-network-based fuzzy inference system.” IEEE Transactions on Systems, Man and Cybernetics 23 (1993): 665–685.
Vernieuwe H., De Baets B., Verhoest N. E. C. "Comparison of clustering algorithms in the identification of Takagi–Sugeno models: A hydrological case study." Fuzzy Sets and Systems 157(2006): 2876 – 2896. https://doi.org/10.1016/j.fss.2006.04.007
Gautam D. K., Holz K. P. "Rainfall-runoff modelling using adaptive neuro-fuzzy systems." Journal of Hydroinformatics 03.1 (January 2001):3-10.
Lee, H.X., and D. Han. "Exploration of neuro-fuzzy models in real time flood forecasting." In: Proceedings of the 2008 International Conference on Artificial Intelligence and Pattern Recognition, ISRST, Orlando, FL, USA, 7–10 July 2008, pp. 264–268. ISBN: 978-1-60651-000-1. .
Akrami S.A., Nourani V., Hakim S.J.S. "Development of nonlinear model based on wavelet-ANFIS for rainfall forecasting at Klang Gates Dam." Water Resources Management 28 (2014): 2999-3018. https://doi.org/10.1007/s11269-014-0651-x.
Wahyuni I., Mahmudy W.F., Iriany A. "Rainfall prediction using hybrid adaptive neuro-fuzzy inference system (ANFIS) and Genetic algorithm." Journal of Telecommunication, Electronic and Computer Engineering 9(2-8) (2017): 51-56.
Haykin S. "Neural networks: A comprehensive foundation". (1999), Prentice-Hall, New Jersey.
Khatibi R., Ghorbani M. A., Hasanpour K.M., Kisi O. "Comparison of three artificial intelligence techniques for discharge routing." Journal of Hydrology 403 (June 2011): 201–212. https://doi.org/10.1016/j.jhydrol.2011.03.007.
Hassanpour K. M. "Flood estimation at ungauged sites using a new hybrid model." Journal of Applied Sciences 9(2008): 1744–1749.
Zurada, J. “Introduction to artificial neural systems” (1992). West Publishing Company, Saint Paul, Minnesota. ISBN:0-314-93391-3.
Hayati M. "Short term load forecasting using artificial neural networks for the west of Iran." Journal of Applied Sciences 12(2007): 1582–1588. doi: 10.3923/jas.2007.1582.1588.
Jang J., Sun C., Mizutani E. “Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence.” (1997). Prentice Hall, New Jersey, U.S.A.
Tsoukalas L. H., Uhrig R. E. "Fuzzy and Neural Approaches in Engineering." (February 1997). Wiley-Interscience, John Wiley & Sons. Inc., New York, USA.
Chang, L.C., Chang, F.J. "Intelligent control for modelling of real-time reservoir operation." Hydrological Processes 15(June 2001), 1621–1634. https://doi.org/10.1002/hyp.226.
Lughole E. “Online Adaptation of Takagi-Sugeno Fuzzy Inference Systems.” (2003). Technical Report, Fuzzy Logic Laboratorium, Linz-Hagenberg.
Legates D. R., McCabe G. J. "Evaluating the use of “Goodness of Fit” measures in hydrologic and hydroclimatic model validation." Water Resources Research 35(January 1999): 233-241. https://doi.org/10.1029/1998WR900018.
Willmott C. J. "On the validation of models." Physical Geography 2 (1981): 184-194.
Willmott C. J., Ackleson S.G., Davis R. E., Feddema, J. J., Klink K. M., Legates D. R., O’Donnell J., Rowe C. M. "Statistics for the evaluation and comparison of models." Journal of Geophysical Research 90 (September 1985): 8995-9005. https://doi.org/10.1029/JC090iC05p08995.
Kessler E., Neas B. "On correlation, with applications to the radar and rain gage measurement of rainfall." Atmospheric Research 34(June 1994): 217-229. https://doi.org/10.1016/0169-8095(94)90093-0.
Legates D. R., Davis R. E. "The continuing search for an anthropogenic climate change signal- Limitations of correlation-based approaches." Geophysical Research Letters 24(September 1997): 2319-2322. https://doi.org/10.1029/97GL02207.
Nash J. E., Sutcliffe J. V. "River Flow Forecasting Through Conceptual Models." Journal of Hydrology 10(1970): 282-290. https://doi.org/10.1016/0022-1694(70)90255-6.
Misra D., Oommen T., Agarwal A., Mishra S.K., Thompson A.M. “Application and analysis of support vector machine based simulation for runoff and sediment yield.” Biosystems Engineering 103 (August 2009): 527–535. https://doi:10.1016/j.biosystemseng.2009.04.017.
DOI: 10.28991/cej-0309159
Refbacks
- There are currently no refbacks.
Copyright (c) 2018 Mahsa H. Kashani, Reza Soltangeys
This work is licensed under a Creative Commons Attribution 4.0 International License.
