Radial Basis Function Neural Networks for Rainfall-Runoff Modeling

K. S Kasiviswanathan

Abstract


Rainfall-runoff process is purely nonlinear and varies spatially as well as temporally. Any hydrological model requires many parameters which represent different components of the process. Availability of all the parameters is difficult for any catchment and probabilistic generation of such type of data is impossible. Under such circumstances, artificial neural networks (ANNs) have proven to be a better tool to model the rainfall-runoff process with minimum available data. The present study is to compare the performance of the model trained with K-means clustering algorithm and modified K-means clustering algorithm. The potential of these two algorithms was tested by developing rainfall runoff models for Vamsadhara river basin located in Andhrapradesh, India. Results of these two models were compared with observed data of Vamsadhara river basin. It is shown that modified K-means clustering algorithm results are more generalized than K-means clustering algorithm results.


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Mahdi Jalili K, Kharaajoo. Proposing a new learning algorithm to improve fault tolerance of neural networks. ICCS, LNCS. 2004; 3037: 717–21p.

Juan Peralta, German Gutierrez, Araceli Sanchis. Design of artificial neural networks based on genetic algorithms to forecast time series. Innovations in Hybrid Intelligent Systems ASC. 2007; 44: 231–8p.

Slawomir Golak. Induced weights artificial neural network. ICANN 2005, LNCS. 2005; 3697; 295–300p.

Armando Vieira. An iterative artificial neural network for high dimensional data analysis. ICANN 2005, LNCS. 2005; 3697: 691–6p.

ASCE Task Committee on Application of Artificial Neural Networks in Hydrology (2000) Artificial neural networks in hydrology–I: Preliminary concepts. Journal of Hydrologic Engineering. 2000; 5(2): 115–23p.

Maier HR, Ashu J, Graeme CD, et al. Methods used for the development of neural networks for the prediction of water resource variables in river systems: Current status and future directions. Environmental Modelling and Software. 2010; 25(8): 891–909p.

Arslan CA. Rainfall–Runoff Modeling Based on Artificial Neural Networks(ANNs). European Journal of Scientific Research. 2011; 64(4): 490–506p.

Kumar ARS, Sudheer KP, Jain SK, et al. Rainfall–runoff modelling using artificial neural networks: comparison of network types. Hydrol. Process 2005; 19: 1277–91p.

Sudheer KP, Gosain AK, Ramasastri KS. A data–driven algorithm for constructing artificial neural network rainfall–runoff models. Hydrol. Processes. 2002; 16(6): 1325–30p.

Nor NIA, Harun S, Kassim AHM. Radial basis function modeling of hourly streamflow hydrograph. Journal of Hydrologic Engineering. 2007; 12(1): 113–23p.

Suhaimi S, Bustami RA. rainfall runoff modeling using radial basis function neural network for Sungai Tinjar catchment, Miri, Sarawak. UNIMAS E–Journal of Civil Engineering. 2009; 1(1).

Fernando DAK, Jayawardena AW. Runoff forecasting using RBF networks with OLS algorithm. J.Hydrol. Engng ASCE. 1998; 3(3): 203–9p.

Nash JE, Sutcliffe JV. River flow forecasting through conceptual models. Hydrological Science Journal. 1970; 41(3): 399–417p.

Ralph B. On the convergence of derivatives of B–splines to derivatives of the Gaussian function. Computational and Applied Mathematics. 2008; 27(1): 79–92p.

Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back–propagation errors. Nature. 1986; 323: 533–6p.

Agarwal A, Singh RD. Runoff modeling through back propagation artificial neural network with variable rainfall–runoff data. Water Resources Management. 2004; 18(3): 285–300p.

Agarwal A, Singh RD, Mishra SK, et al. ANN–based sediment yield models for Vamsadhara river basin (India). Journal of Biosystems Engineering. 2005; 31(1): 95–100p.




DOI: https://doi.org/10.3759/jowrem.v1i2.1786

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