Estimation of Reservoir Discharge with the Help of Clustered Neurogenetic Algorithm
This chapter presents a new approach of reservoir out flow prediction using a clustered neurogenetic algorithm. The algorithm combines the learning ability of artificial neural networks with searching capability of the genetic algorithm. The model is tested on the Panchet reservoir in river Damodar using the historical, hydrological, and water supply dataset. The values of the input parameters are classified into six groups based on the magnitude of the input parameters. The results showed a highly adaptive and flexible investigating ability of the model in prediction of nonlinear relationships among different variables.
KeywordsClassified neurogenetic models discharge model performance multi-reservoirs
The authors would like to acknowledge Dr. Chandan Ray, Retd. Chief Engineer, Irrigation and Drainage Department, West Bengal Govt. and Dr. Debasri Roy, Joint Coordinator, School of Water Resources Engineering, Jadavpur University, West Bengal, India for their valued comments and reviews, which helped in the preparation of the chapter.
- Bhatt VK, Bhattacharya P, Tiwari AK (2007) Application of artificial neural network in estimation of rainfall erosivity. Hydrol J 1–2:30–39Google Scholar
- Hassoun MH (1995) Fundamentals of artificial neural networks. MIT Press, London, p 1Google Scholar
- Lahiri-Dutt K (2000) State and the community in water management case of the Damodar Valley Corporation, India. Report on Resource Management in Asia Pacific Program, The Australian National UniversityGoogle Scholar
- Majumder M, Roy P, Mazumdar A (2007) Optimization of the water use in the river Damodar In West Bengal In India: an integrated multi-reservoir system with the help of artificial neural network. J Eng Comput Architect 1(2): Article no.1192Google Scholar
- Singh VP (1995) Computer models of watershed hydrology. Water Resource Publications, Highlands Ranch, COGoogle Scholar
- World Metereological Organization (WMO) (1992) Simulated real-time intercomparison of hydrological models. Retrieved from http://www.wmo.int/e-catalog/detail_en.php?PUB_ID=73&SORT=N&q= on 25th June 2008