Open Access
Issue
MATEC Web Conf.
Volume 111, 2017
Fluids and Chemical Engineering Conference (FluidsChE 2017)
Article Number 01007
Number of page(s) 7
Section Advances in Fluids Flow and Mechanics
DOI https://doi.org/10.1051/matecconf/201711101007
Published online 20 June 2017
  1. Aqil, M., et al., Neural Networks for Real Time Catchment Flow Modeling and Prediction. Water Resources Management, 2007. 21(10): p. 1781–1796. [CrossRef] [Google Scholar]
  2. Kentel, E., Estimation of river flow by artificial neural networks and identification of input vectors susceptible to producing unreliable flow estimates. Journal of Hydrology, 2009. 375(3–4): p. 481–488. [CrossRef] [Google Scholar]
  3. Jain, A. and A.M. Kumar, Hybrid neural network models for hydrologic time series forecasting. Applied Soft Computing, 2007. 7(2): p. 585–592. [Google Scholar]
  4. Nilsson, P., C.B. Uvo, and R. Berndtsson, Monthly runoff simulation: Comparing and combining conceptual and neural network models. Journal of Hydrology, 2006. 321(1–4): p. 344–363. [CrossRef] [Google Scholar]
  5. Kneis, D., A lightweight framework for rapid development of object-based hydrological model engines. Environmental Modelling & Software, 2015. 68(0): p. 110–121. [CrossRef] [Google Scholar]
  6. Samsudin, R., P. Saad, and A. Shabri, River flow time series using least squares support vector machines. Hydrol. Earth Syst. Sci., 2011. 15(6): p. 1835–1852. [CrossRef] [Google Scholar]
  7. Firat, M. and M.E. Turan, Monthly river flow forecasting by an adaptive neuro-fuzzy inference system. Water and Environment Journal, 2010. 24(2): p. 116–125. [CrossRef] [Google Scholar]
  8. Akhtar, M.K., et al., River flow forecasting with artificial neural networks using satellite observed precipitation pre-processed with flow length and travel time information: case study of the Ganges river basin. Hydrol. Earth Syst. Sci., 2009. 13(9): p. 1607–1618. [CrossRef] [Google Scholar]
  9. Nativi, S., et al., Big Data challenges in building the Global Earth Observation System of Systems. Environmental Modelling & Software, 2015. 68(0): p. 1–26. [CrossRef] [Google Scholar]
  10. Seyam, M. and Y. Mogheir, Application of artificial neural networks model as analytical tool for groundwater salinity. Journal of Environmental Protection, 2011. 2(01): p. 56. [CrossRef] [Google Scholar]
  11. Solomatine, D., L.M. See, and R.J. Abrahart, Data-Driven Modelling: Concepts, Approaches and Experiences, in Practical Hydroinformatics, R. Abrahart, L. See, and D. Solomatine, Editors. 2008, Springer Berlin Heidelberg. p. 17–30. [Google Scholar]
  12. Kisi, O., et al., Intermittent Streamflow Forecasting by Using Several Data Driven Techniques. Water Resources Management, 2012. 26(2): p. 457–474. [CrossRef] [Google Scholar]
  13. Daniel, E.B., et al., Watershed Modeling and its Applications: A State-of-the-Art Review. The Open Hydrology Journal, 2011. 5: p. 26–50. [CrossRef] [Google Scholar]
  14. Kanevski, M., et al., Environmental data mining and modeling based on machine learning algorithms and geostatistics. Environmental Modelling & Software, 2004. 19(9): p. 845–855. [CrossRef] [Google Scholar]
  15. Seyam, M. and F. Othman, Long-term variation analysis of a tropical river’s annual streamflow regime over a 50-year period. Theoretical and Applied Climatology, 2015. 121(1): p. 71–85. [CrossRef] [Google Scholar]
  16. Lee, C.M., Master Plan Study on Flood Mitigation and River Management for Sg. Selangor River Basin., 2002, Drainage and Irrigation Department (DID) Malaysia. [Google Scholar]
  17. Hassan, A.J., A.A. Ghani, and R. Abdullah, Development Of Flood Risk Map Using GIS For Sg. Selangor Basin, 2004, National Hydraulic Research Institute of Malaysia: Malaysia. [Google Scholar]
  18. Subramaniam, V., Managing Water Supply In Selangor And Kuala Lumpur, in BULETIN INGENIEUR2004, THE BOARD OF ENGINEERS MALAYSIA: 50580 Kuala Lumpur, Malaysia. p. 12–20. [Google Scholar]
  19. Seyam, M. and F. Othman, The Influence of Accurate Lag Time Estimation on the Performance of Stream Flow Data-driven Based Models. Water Resources Management, 2014. 28(9): p. 2583–2597. [CrossRef] [Google Scholar]
  20. Seyam, M. and F. Othman, Long-term variation analysis of a tropical river’s annual streamflow regime over a 50-year period. Theoretical and Applied Climatology, 2014. [Google Scholar]
  21. Maier, H.R. and G.C. Dandy, Neural networks for the prediction and forecasting of water resources variables: a review of modelling issues and applications. Environmental Modelling & Software, 2000. 15(1): p. 101–124. [CrossRef] [Google Scholar]
  22. Firat, M., Artificial Intelligence Techniques for river flow forecasting in the Seyhan River Catchment, Turkey. Hydrol. Earth Syst. Sci. Discuss., 2007. 4(3): p. 1369–1406. [CrossRef] [Google Scholar]
  23. Behzad, M., K. Asghari, and E.A. Coppola Jr, Comparative Study of SVMs and ANNs in Aquifer Water Level Prediction. Journal of Computing in Civil Engineering, 2010. 24: p. 408. [CrossRef] [Google Scholar]
  24. Asefa, T., et al., Multi-time scale stream flow predictions: The support vector machines approach. Journal of Hydrology, 2006. 318(1-4): p. 7–16. [CrossRef] [Google Scholar]
  25. Yu, P.-S., S.-T. Chen, and I.F. Chang, Support vector regression for real-time flood stage forecasting. Journal of Hydrology, 2006. 328(3–4): p. 704–716. [Google Scholar]
  26. Wu, C.L., K.W. Chau, and Y.S. Li, River stage prediction based on a distributed support vector regression. Journal of Hydrology, 2008. 358(1–2): p. 96–111. [Google Scholar]
  27. Asefa, T., et al., Multi-time scale stream flow predictions: The support vector machines approach. Journal of Hydrology, 2006. 318(1–4): p. 7–16. [CrossRef] [Google Scholar]
  28. Chen, S.-T. and P.-S. Yu, Pruning of support vector networks on flood forecasting. Journal of Hydrology, 2007. 347(1–2): p. 67–78. [CrossRef] [Google Scholar]
  29. Lin, J.Y., C.T. Cheng, and K.W. Chau, Using support vector machines for long-term discharge prediction. Hydrological Sciences Journal, 2006. 51(4): p. 599–612. [Google Scholar]
  30. Basketfield, D. and N. She, Long Range Forecast of Streamflow Using Support Vector Machine, in Impacts of Global Climate Change. 2005. p. 1–9. [Google Scholar]
  31. Guo, J., et al., Monthly streamflow forecasting based on improved support vector machine model. Expert Systems with Applications, 2011. 38(10): p. 13073–13081. [CrossRef] [Google Scholar]
  32. Noori, R., et al., Assessment of input variables determination on the SVM model performance using PCA, Gamma test, and forward selection techniques for monthly stream flow prediction. Journal of Hydrology, 2011. 401(3–4): p. 177–189. [CrossRef] [Google Scholar]
  33. Shabri, A. and Suhartono, Streamflow forecasting using least-squares support vector machines. Hydrological Sciences Journal, 2012. 57(7): p. 1275–1293. [CrossRef] [Google Scholar]
  34. Ch, S., et al., Streamflow forecasting by SVM with quantum behaved particle swarm optimization. Neurocomputing, 2013. 101(0): p. 18–23. [CrossRef] [Google Scholar]
  35. Tiwari, M.K. and C. Chatterjee, Development of an accurate and reliable hourly flood forecasting model using wavelet–bootstrap–ANN (WBANN) hybrid approach. Journal of Hydrology, 2010. 394(3–4): p. 458–470. [CrossRef] [Google Scholar]
  36. Perugu, M., A. Singam, and C. Kamasani, Multiple Linear Correlation Analysis of Daily Reference Evapotranspiration. Water Resources Management, 2013. 27(5): p. 1489–1500. [CrossRef] [Google Scholar]
  37. Seyam, M., F. Othman, and A. El-Shafie, RBFNN Versus Empirical Models for Lag Time Prediction in Tropical Humid Rivers. Water Resources Management, 2016. [Google Scholar]

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