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    Discussion of “Comparison between Response Surface Models and Artificial Neural Networks in Hydrologic Forecasting” by Jianjun Yu, Xiaosheng Qin, Ole Larsen, and L. H. C. Chua 

    Source: Journal of Hydrologic Engineering:;2015:;Volume ( 020 ):;issue: 005
    Author(s): N. Sajikumar
    Publisher: American Society of Civil Engineers
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    Classification of River Basins Using Artificial Neural Network 

    Source: Journal of Hydrologic Engineering:;2000:;Volume ( 005 ):;issue: 003
    Author(s): B. S. Thandaveswara; N. Sajikumar
    Publisher: American Society of Civil Engineers
    Abstract: Hydrological homogeneity is often assumed whenever there is lack of data in a basin and hydrologic analysis is carried out utilizing the data from neighboring basins. However, the neighboring basins may not be hydrologically ...
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    Closure to “Classification of River Basins Using Artificial Neural Network” by B. S. Thandaveswara and N. Sajikumar 

    Source: Journal of Hydrologic Engineering:;2003:;Volume ( 008 ):;issue: 002
    Author(s): N. Sajikumar; B. S. Thandaveswara
    Publisher: American Society of Civil Engineers
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    Influence of Fluid–Structure Interaction on Pressure Fluctuations in Transient Flow 

    Source: Journal of Pipeline Systems Engineering and Practice:;2021:;Volume ( 012 ):;issue: 002:;page 04021002-1
    Author(s): Miji Cherian R; N. Sajikumar; K. S. Sumam
    Publisher: ASCE
    Abstract: Sudden changes in operating conditions of a piping system induce a fluid hammer with cyclic pressure fluctuations that moves back and forth and finally dies out. Although one-dimensional continuity and momentum equations ...
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