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Computational Intelligence based Data-Driven Modeling: A case Study in Hydrology
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 180 - Number 43 |
| Year of Publication: 2018 |
| Authors: Tanveer Ahmed Siddiqi, Muhammad Jawed Iqbal |
10.5120/ijca2018917127
|
Tanveer Ahmed Siddiqi, Muhammad Jawed Iqbal . Computational Intelligence based Data-Driven Modeling: A case Study in Hydrology. International Journal of Computer Applications. 180, 43 ( May 2018), 11-15. DOI=10.5120/ijca2018917127
Abstract
Nonlinearity exists in most of the real phenomenon and it is difficult to model the behaviour of such indefinite systems. Neural computing is one the important tools for modeling the nonlinear structures and efficiently applying in the measurement of inexplicit systems. The monsoon rainfall in Pakistan shows an important part in upstream flow in the Upper Indus Basin (UIB). This study, suggests different Dynamic Neural Network (DNN) models, based on time delayed autoregressive structures, for the upstream water flow of Tarbela Dam on upper Indus basin. The appropriateness of the models for training, validation and testing phases established on evaluation metrics which exhibit the accuracy of the models. This paper also gives a major preference when only the upstream flow gauge stations data are available, which can be beneficial for water-resource engineers.
References
- Khan, B., Iqbal, M. J., and Yosufzai, M. A. Y. 2011 Flood risk assessment of river Indus of Pakistan, Arab J Geosci. 4, 115-122.
- Hassan, S. A. and Ansari, M. R. K. 2010 Nonlinear analysis of seasonality and stochasticity of the Indus River, Hydro. Sci. J. 55, 250-265.
- Coulibaly, P., Anctil, F. and Bobfe, B. 2001 Multivariate reservoir inflow forecasting using temporal neural networks, J. Hydrol. Eng., 6, 367.
- Smith K. 1997 Environmental hazards, Assessing Risk and Reducing Disaster, 2nd ed., Routledge: London & New York.
- Maier, H. R. and Dandy, G. C. 1997 Modelling cyano bacteria (blue green algae) in the River Murray using artificial neural networks, Mathematics and Computers in Simulation 43, 377–386.
- Tokar A. S. and Johnson, P. A. 1999 Rainfall-Runoff Modeling using Artificial Neural Networks, J Hydrol. Eng. ASCE 4, 232-239.
- Mellit, A., Pavan, A. M. and Benghanem, M. 2013 Least squares support vector machine for short-term prediction of meteorological time series, Theoretical and applied climatology 111, 297-307.
- Ivakhnenko, A. G. and Grigorʹevich L. V. 1967 Cybernetics and forecasting techniques, American Elsevier Pub. Co.
- Haykin, S. 2009 Neural Networks: A Comprehensive Foundation, eight ed., Pearson Prentice Hall, India.
- Levenberg, K. 1944 A Method for the Solution of Certain Non-Linear Problems in Least Squares. Quarterly of Applied Mathematics, 2, 164–168.
- Marquardt, D. 1963 An Algorithm for Least-Squares Estimation of Nonlinear Parameters, SIAM Journal on Applied Mathematics, 11 (2), 431–441. doi:10.1137/0111030.
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