NDLI: Estimation of missing annual discharge

Content Provider	IEEE Xplore Digital Library
Author	Hedayatizade, M. Golestani, M. Kavianpour, M.R. Abdi, M.S.
Copyright Year	2010
Description	Author affiliation: Civil Eng., University of Kurdistan, Sanandaj, Iran (Abdi, M.S.) \|\| Civil and Structural Mng, K.N.Toosi University of Technology, Tehran, Iran (Golestani, M.) \|\| Civil Eng., K.N.Toosi University of Technology, Tehran, Iran (Kavianpour, M.R.) \|\| Civil Eng., K.N.Toosi University of Technology, Karaj, Iran (Hedayatizade, M.)
Abstract	Flood is one of the well-known facts which endanger the lives and human resources around the world. Thus, accurate estimation of flood discharge in every region can lead to more precise hydraulic structures with adequate capacity to avoid the above problems. Usually, the estimation of flood capacity in any station required sufficient data. However, the lake of sufficient and long-term hydrological data in many situations is a major threat to the start new projects. Thus, it is necessary to develop new methods for different circumstances and situations to estimate the required data for the target station. In this study artificial neural network has been applied to the reconstruction of annual discharge of hydrometric stations in Urmia Lake Basin and the results have been compared with those of normal ratio method to introduce the best technique for this study. It was shown that neural network provides the best approximation based on the root mean square of the estimated errors (RMSE), the percent of volume error (VE), and the correlation coefficient (R2).
Starting Page	38
Ending Page	43
File Size	2218295
Page Count	6
File Format	PDF
ISBN	9781424486199
e-ISBN	9781424486212
DOI	10.1109/ICEEA.2010.5596086
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-10
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Missing data Annual discharge Normal ratio method Artificial neural networks Lakes Lake Urmia Yttrium
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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