NDLI: Optimizing Operation Rules of Sluices in River Networks Based on Knowledge-driven and Data-driven Mechanism

Content Provider	Springer Nature Link
Author	Gu, Zhenghua Cao, Xiaomeng Liu, Guoliang Lu, Weizhen
Copyright Year	2014
Abstract	Many river networks are controlled by sluices, especially in plain area. To prevent potential floods, maintain water level, and improve water environment in the inner river, the water transfer of river networks is needed and executed often in terms of optimized operation rules of sluices planned in advancing. To guarantee maintaining the optimized operation status, the provision of appropriate operating framework of sluices in river networks is necessary and presented in this study based on the knowledge-driven and data-driven mechanism. The general framework is formed by River Networks Mathematical Model (RNMM), Artificial Neural Networks (ANN) and Genetic Algorithms (GA), in which, ANN is used to build a rapid simulation of the flow variables in river networks, RNMM is used to train the ANN model, and GA method, whose fitness function is constructed by the ANN model, is used to optimize the operation rules of sluices. As a demonstration, the framework was applied to water transfer project of the tidal river networks locating in Pudong New Area of Shanghai in mainland China. Firstly, RNMM of Pudong was built and validated according to observed data during water transfer tests. Then, the Backward Propagation Neural Networks (BPNN) model was established as the fast simulation tool of flow variables of river networks through the numerical experiments with RNMM. The Generalized Genetic Algorithms (GGA) was recommended as optimization algorithm of sluices operation rules. Through comparing the optimization results with the RNMM simulation outputs under eight cases, it is verified that the framework can offer sub-optimal operation rules of sluices in river networks and present excellent speediness, robustness and flexibility. It is encouraged to be applied to more complicated, practical problems.
Starting Page	3455
Ending Page	3469
Page Count	15
File Format	PDF
ISSN	09204741
Journal	Water Resources Management
Volume Number	28
Issue Number	11
e-ISSN	15731650
Language	English
Publisher	Springer Netherlands
Publisher Date	2014-07-08
Publisher Institution	European Water Resources Association (EWRA)
Publisher Place	Dordrecht
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Optimizing operation rules River networks Sluices General framework Mathematical model Artificial neural networks Genetic algorithms Pudong Water transfer Hydrogeology Hydrology/Water Resources Geotechnical Engineering & Applied Earth Sciences Atmospheric Sciences Civil Engineering Environment
Content Type	Text
Resource Type	Article
Subject	Water Science and Technology Civil and Structural Engineering

Sl.	Authority	Responsibilities	Communication Details
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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