Streamflow Forecasting Using Artificial Neural Network and Support Vector Machine Models
Keywords:
Streamflow, Soft computing models, ANN, SVM.Abstract
This paper investigates the ability of two soft computing methods including artificial neural network (ANN) and support vector machine (SVM) models in modeling monthly streamflow. The results of ANN and SVM models are compared on basis of determination coefficient (R2), root mean square error (RMSE) and mean absolute error (MAE) to evaluate the performance of the applied models. Comparison of results indicates that the SVM models with RMSE = 147.01 m3/s, MAE = 86.68 m3/s and R2 = 0.872 in test period is superior in forecasting monthly streamflows than the ANN models with RMSE = 161.59 m3/s, MAE = 94.87 m3/s and R2 = 0.869, respectively. It is found that SVM models can be successfully used in predicting monthly streamflows.
References
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[2]. Hsu Kl, Gupta HV, Sorooshian S: Artificial neural network modeling of the rainfall?runoff process. Water resources research 1995, 31:2517-2530.
[3]. Karunanithi N, Grenney WJ, Whitley D, Bovee K: Neural networks for river flow prediction. Journal of computing in civil engineering 1994, 8:201-220.
[4]. Zealand CM, Burn DH, Simonovic SP: Short term streamflow forecasting using artificial neural networks. Journal of hydrology 1999, 214:32-48.
[5]. Sivakumar B, Jayawardena A, Fernando T: River flow forecasting: use of phase-space reconstruction and artificial neural networks approaches. Journal of hydrology 2002, 265:225-245.
[6]. Ki?i Ö: River flow modeling using artificial neural networks. Journal of Hydrologic Engineering 2004, 9:60-63.
[7]. Tayyab M, Zhou J, Zeng X, Adnan R: Discharge Forecasting By Applying Artificial Neural Networks At The Jinsha River Basin, China. European Scientific Journal 2016, 12.
[8]. Cigizoglu HK, Ki?i Ö: Flow prediction by three back propagation techniques using k-fold partitioning of neural network training data. Hydrology Research 2005, 36:49-64.
[9]. Khan MS, Coulibaly P: Application of support vector machine in lake water level prediction. Journal of Hydrologic Engineering 2006, 11:199-205.
[10]. Lin J-Y, Cheng C-T, Chau K-W: Using support vector machines for long-term discharge prediction. Hydrological Sciences Journal 2006, 51:599-612.
[11]. Wu C, Chau K, Li Y: River stage prediction based on a distributed support vector regression. Journal of hydrology 2008, 358:96-111.
[12]. Cimen M: Estimation of daily suspended sediments using support vector machines. Hydrological Sciences Journal 2008, 53:656-666.
[13]. KI?I, OZGUR, and Mesut Cimen. "Evapotranspiration modelling using support vector machines/Modélisation de l'évapotranspiration à l'aide de ‘support vector machines’." Hydrological sciences journal 54.5 (2009): 918-928.
[14]. Karamouz M, Ahmadi A, Moridi A: Probabilistic reservoir operation using Bayesian stochastic model and support vector machine. Advances in water resources 2009, 32:1588-1600.
[15]. Chen S-T, Yu P-S, Tang Y-H: Statistical downscaling of daily precipitation using support vector machines and multivariate analysis. Journal of Hydrology 2010, 385:13-22.
[16]. Guo J, Zhou J, Qin H, Zou Q, Li Q: Monthly streamflow forecasting based on improved support vector machine model. Expert Systems with Applications 2011, 38:13073-13081.
[17]. Singh K, Pal M, Ojha C, Singh V: Estimation of removal efficiency for settling basins using neural networks and support vector machines. Journal of Hydrologic Engineering 2008, 13:146-155.
[18]. Liong SY, Sivapragasam C: Flood stage forecasting with support vector machines1. Wiley Online Library; 2002.
[19]. McCulloch WS, Pitts W: A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics 1943, 5:115-133.
[20]. Hebb D: The organisation of behavior Wiley. New York 1949.
[21]. Rosenblatt F: The perceptron: A probabilistic model for information storage and organization in the brain. Psychological review 1958, 65:386.
[22]. Minsky M, Papert S: Perceptrons: An essay in computational geometry. Cambridge, MA: MIT Press; 1969.
[23]. Werbos P: Beyond regression: New tools for prediction and analysis in the behavioral sciences. 1974.
[24]. Rumelhart D, Hinton G, Williams R: Learning internal representation by back propagation. Parallel distributed processing: exploration in the microstructure of cognition 1986, 1.
[25]. Gallant SI: Neural network learning and expert systems. MIT press; 1993.
[26]. Vapnik V: The nature of statistical learning theory. Springer science & business media; 2013.
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Published
2017-03-31
How to Cite
Adnan, R. M., Yuan, X., Kisi, O., & Yuan, Y. (2017). Streamflow Forecasting Using Artificial Neural Network and Support Vector Machine Models. American Scientific Research Journal for Engineering, Technology, and Sciences, 29(1), 286–294. Retrieved from https://asrjetsjournal.org/index.php/American_Scientific_Journal/article/view/2814
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