References

  1. M. Aqil, I. Kita, A. Yano, S. Nishiyama, Neural networks for real time catchment flow modeling and prediction, Water Resour. Manage., 21(10) (2007) 1781–1796.
  2. E. Kentel, Estimation of river flow by artificial neural networks and identification of input vectors susceptible to producing unreliable flow estimates, J. Hydrology, 375(3–4) (2009) 481– 488.
  3. D. Solomatine, L.M. See, R.J. Abrahart, eds. Data-driven Modelling: Concepts, Approaches and Experiences in Practical Hydroinformatics, 2008, Springer Berlin Heidelberg. p. 17–30.
  4. O. Kisi, A.M. Nia, M.G. Gosheh, M.R.J. Tajabadi, A. Ahmadi, Intermittent stream flow forecasting by using several data driven techniques, Water Resour. Manage., 26(2) (2012) 457–474.
  5. E.B. Daniel, J.V. Camp, E.J. LeBoeuf, J.R. Penrod, J.P. Dobbins, M.D. Abkowtz, Watershed modeling and its applications: a state-of-the-art review, Open Hydrology J., 5 (2011) 26–50.
  6. M.E. Turan, M.A. Yurdusev, River flow estimation from upstream flow records by artificial intelligence methods, J. Hydrology, 369(1–2) (2009) 71–77.
  7. A. Bhadra, A. Bandyopadhyay, R. Singh, N.S. Raghuwanshi, Rainfall-runoff modeling: comparison of two approaches with different data requirements, Water Resour. Manage., 24(1) (2010) 37–62.
  8. G.R. Rakhshanehroo, M. Vaghefi, M.M. Shafiee, Flood forecasting in similar catchments using neural networks, Turkish J. Eng. Environ. Sci., 34(1) (2010) 57–66.
  9. L.E. Besaw, D.M. Rizzo, P.R. Bierman, W.R. Hackett, Advances in ungauged streamflow prediction using artificial neural networks, J. Hydrology, 386(1–4) (2010) 27–37.
  10. E. Triana, J. Labadie, T. Gates, C. Anderson, Neural network approach to stream-aquifer modeling for improved river basin management, J. Hydrology, 391(3–4) (2010) 235–247.
  11. D. Edossa, M. Babel, Application of ANN-based streamflow forecasting model for agricultural water management in the Awash River basin, Ethiopia. Water Resour. Manage., 25(6) (2011) 1759–1773.
  12. M. Sahu, K.K. Khatua, S.S. Mahapatra, A neural network approach for prediction of discharge in straight compound open channel flow, Flow Measure. Instrum., 22(5) (2011) 438–446.
  13. F. Machado, M. Mine, E. Kaviski, H. Fill, Monthly rainfall–runoff modelling using artificial neural networks, Hydrol. Sci. J., 56(3) (2011) 349–361.
  14. O. Kisi, C. Ozkan, B. Akay, Modeling discharge–sediment relationship using neural networks with artificial bee colony algorithm, J. Hydrology, 428–429(0) (2012) 94–103.
  15. S. Wei, H. Yang, J.X. Song, K. Abbaspour, Z.X. Zue, A wavelet- neural network hybrid modelling approach for estimating and predicting river monthly flows, Hydrol. Sci. J., 58(2) (2013) 374–389.
  16. J. Adamowski, K. Sun, Development of a coupled wavelet transform and neural network method for flow forecasting of non-perennial rivers in semi-arid watersheds, J. Hydrology, 390(1–2) (2010) 85–91.
  17. S.K. Jain, V.P. Singh, M.T. van Genuchten, Analysis of soil water retention data using artificial neural networks, J. Hydrol. Eng., 9(5) (2004) 415–420.
  18. D.B. May, M. Sivakumar, Prediction of urban stormwater quality using artificial neural networks, Environ. Model. Software, 24(2) (2009) 296–302.
  19. Y.B. Dibike, D.P. Solomatine, River flow forecasting using artificial neural networks, Phys. Chem. Earth, Part B: Hydrology, Oceans Atmosphere, 26(1) (2001) 1–7.
  20. C.M. Lee, Master Plan Study on Flood Mitigation and River Management for Sg. Selangor River Basin. 2002, Drainage and Irrigation Department (DID) Malaysia.
  21. A.J. Hassan, A.A. Ghani, R. Abdullah, Development of Flood Risk Map Using GIS for Sg. Selangor Basin., National Hydraulic Research Institute of Malaysia: Malaysia.2004.
  22. R. Samsudin, P. Saad, A. Shabri, River flow time series using least squares support vector machines, Hydrol. Earth Syst. Sci., 15(6) (2011) 1835–1852.
  23. V. Subramaniam, Managing water supply in Selangor and Kuala Lumpur, in Buletin Ingenieur. 2004, The Board of Engineers Malaysia: 50580 Kuala Lumpur, Malaysia. p. 12–20.
  24. A. Shafie, Extreme Flood Event: A Case Study on Floods of 2006 and 2007 in Johor, Malaysia. 2009, Colorado State University: Fort Collins, Colorado, USA.
  25. M.T.J.v. Breemen, Salt intrusion in the Selangor Estuary in Malaysia. 2008, University of Twente: The Netherlands.
  26. W. Nelson, Bruce, An unusual turbidity maximum, in Proc. Marine Science, C.W. Johan and K. Cees, eds., 2002, Elsevier. p. 483–497.
  27. M.K. Tiwari, C. Chatterjee, Development of an accurate and reliable hourly flood forecasting model using wavelet–bootstrap–ANN (WBANN) hybrid approach, J. Hydrology, 394(3–4) (2010) 458–470.
  28. H.R. Maier, G.C. Dandy, Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications, Environ. Model. Software, 15(1) (2000) 101–124.
  29. M. Firat, Artificial intelligence techniques for river flow forecasting in the Seyhan River catchment, Turkey. Hydrol. Earth Syst. Sci. Discuss., 4(3) (2007) 1369–1406.
  30. K.P. Sudheer, A.K. Gosain, K.S. Ramasastri, A data-driven algorithm for constructing artificial neural network rainfall-runoff models, Hydrol. Processes, 16(6) (2002) 1325–1330.
  31. M. Perugu, A. Singam, C. Kamasani, Multiple linear correlation analysis of daily reference evapotranspiration, Water Resour. Manage., 27(5) (2013) 1489–1500.