Dynamics of surface runoff and its influence on the water quality using competitive algorithms in artificial neural networks

References

• Chung , S. O. , Kim , H. S. and Kim , J. S. 2003 . Model development for nutrient loading from paddy rice fields . Agric. Water Mgnt. , 62 : 1 – 17 .
   Web of Science ®Google Scholar
• Santhi , C. , Arnold , J. G. , Williams , J. R. , Hauck , L. M. and Dugas , W. A. 2001 . Application of a watershed model to evaluate management effects on point and nonpoint source pollution . Trans. ASAE , 44 ( 6 ) : 1559 – 1570 .
   Google Scholar
• Sudheer , K. P. , Gosain , A. K. and Rmasastri , K. S. 2002 . A data-driven algorithm for constructing artificial neural network rainfall-runoff models . Hydrologic Proc. , 16 ( 6 ) : 1325 – 1330 .
   Web of Science ®Google Scholar
• Cigizoglu , H. K. and Kisi , O. 2005 . Flow prediction by two back propagation techniques using k-fold Partitioning of Neural Network Training Data . Nordic Hydrol. , 36 ( 1 ) : 1 – 16 .
   Google Scholar
• Basheer , I. A. and Hajmeer , M. 2000 . Artificial neural networks: fundamentals, computing, design, and application . J. Microbiol. Meth. , 43 : 3 – 31 .
   PubMed Web of Science ®Google Scholar
• Water Science and Technology Library . 2000 . Artificial Neural Networks in Hydrology , Edited by: Govindaraju , R. S. and Ramachandra Rao , A. Dordrecht , , The Netherlands : Kluwer Academic Publishers .
   Google Scholar
• Solomatine , D. P. Data-driven modeling: paradigm, methods, experiences . Proceedings of the 5th Int. Conference on Hydroinformatics . pp. 757 – 763 . Cardiff , , UK
   Google Scholar
• Jain , A. K. , Mao , J. and Mohiuddin , K. M. 1996 . Artificial neural networks: a tutorial , 31 – 44 . Institute of Electrical and Electronics Engineers .
   Google Scholar
• Sargent , D. J. 2001 . Comparison of Artificial Neural Networks with other statistical approaches . Cancer supplement , 91 ( 8 ) : 1636 – 1642 .
   Web of Science ®Google Scholar
• Azam , F. 2000 . Biologically inspired modular neural networks , Ph.D. dissertation The Virginia Polytechnic Institute and State University .
   Google Scholar
• Specht , D. F. 1991 . A general regression neural network . IEEE Transactions on neural networks , 2 ( 16 ) : 568 – 576 .
   PubMed Web of Science ®Google Scholar
• Marquez , L. and Hill , T. 1991 . Function approximation using backpropagation and general regression neural network . Proc. 26th Annual Hawaii International Conference on System Science , 4 : 607 – 615 .
   Google Scholar
• Kim , M.-Y. , Seo , M.-C. and Kim , M.-K. 2006 . Linking hydro-meteorological factors to the assessment of nutrient loadings to streams from large-plotted paddy rice fields , Agricultural Water Management . in press
   Google Scholar
• Poirazi , P. , Neocleous , C. , Pattichis , C. S. and Schizas , C. N. 2004 . Classification capacity of a modular neural network implementing neurally inspired architecture and training rules . IEEE Trans. Neural Networks , 15 ( 3 ) : 597 – 612 .
   PubMed Web of Science ®Google Scholar
• Brikundavyi , S. , Labib , R. , Trung , H. T. and Rousselle , J. 2002 . Performance of neural networks in daily streamflow forecasting . J. Hydrol. Engng. ASCE , 7 ( 5 ) : 392 – 398 .
   Web of Science ®Google Scholar
• Cigizoglu , H. K. 2004 . Discussion of “Performance of Neural Networks in Daily Streamflow Forecasting” . ASCE J. of Hydrol. Eng. , 9 ( 6 ) : 556 – 557 . Brikundavyi, S., Labib, R., H. T. Trung, and Rousselle, J.
   Web of Science ®Google Scholar
• Kehagias , A. and Petridis , V. 1991 . Time-series segmentation using predictive modular neural networks . Neural Comput. , 9 : 1691 – 1709 .
   Web of Science ®Google Scholar