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Simulation with RBF Neural Network Model for Reservoir Operation Rules

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Abstract

Reservoirs usually have multipurpose, such as flood control, water supply, hydropower and recreation. Deriving reservoirs operation rules are very important because it could help guide operators determine the release. For fulfilling such work, the use of neural network has presented to be a cost-effective technique superior to traditional statistical methods. But their training, usually with back-propagation (BP) algorithm or other gradient algorithms, is often with certain drawbacks. In this paper, a newly developed method, simulation with radial basis function neural network (RBFNN) model is adopted. Exemplars are obtained through a simulation model, and RBF neural network is trained to derive reservoirs operation rules by using particle swarm optimization (PSO) algorithm. The Yellow River upstream multi-reservoir system is demonstrated for this study.

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References

  • Ahmed JA, Sarma AK (2007) Artificial neural network model for synthetic stream flow generation. Water Resour Manage 21:1015–1029

    Article  Google Scholar 

  • Bhaskar NR, Whitlatch EE Jr (1980) Derivation of monthly reservoir release. Water Resour Res 16(6):987–993

    Article  Google Scholar 

  • Bower BT, Hufschmidt MM, Reedy WW (1966) Operating procedures: their role in the design of water-resource systems by simulation analyses. In: Maass A et al (eds) Design of water-resource systems. Harvard University Press, Cambridge, pp 443–458

    Google Scholar 

  • Chandramouli V, Deka P (2005) Neural network based decision support model for optimal reservoir operation. Water Resour Manage 19:447–464

    Article  Google Scholar 

  • Chandramouli V, Raman H (2001) Multi reservoir modeling with dynamic programming and neural network. J Water Resour Plan Manage 127(3):89–98

    Article  Google Scholar 

  • Chen S, Cowan CFN, Grant PM (1991) Orthogonal least squares learning algorithm for radial basis function networks. IEEE Trans Neural Netw 2:302–309

    Article  Google Scholar 

  • Hufschmidt MM, Fiering MB (1966) Simulation techniques for design of water-resources systems. Harvard University Press, Cambridge, MA

    Google Scholar 

  • Karamouz M, Houck MH (1982) Annual and monthly reservoir operating rules generated by deterministic optimization. Water Resour Res 18(5):1337–1344

    Article  Google Scholar 

  • Karayiannis NB, Mi GW (1997) Growing radial basis networks: merging supervised and unsupervised learning with network growth techniques. IEEE Trans Neural Netw 8(6):1492–1506

    Article  Google Scholar 

  • Karunanithi N, Grenney WJ, Whitley D, Bovee K (1994) Neural network for river flow prediction. J Comput Civ Eng ASCE 8(1):201–220

    Article  Google Scholar 

  • Kubat M (1998) Decision trees can initialize radial-basis function networks. IEEE Trans Neural Netw 9:813–821

    Article  Google Scholar 

  • Loucks DP, Sigvaldason OT (1981) Multiple-reservoir operation in North America. Water Sci Technol 1:711–728

    Google Scholar 

  • Mahmut F, Mehmet AY, Mustafa ET (2009) Evaluation of artificial neural network techniques for municipal water consumption modeling. Water Resour Manage 23:617–632

    Article  Google Scholar 

  • Rai RK, Mathur BS (2008) Event-based sediment yield modeling using artificial neural network. Water Resour Manage 22(4):423–441

    Article  Google Scholar 

  • Raman H, Chandramouli V (1996) Deriving a general operating policy for reservoirs using neural network. J Water Resour Plan Manage ASCE 122(5):342–347

    Article  Google Scholar 

  • Ranjithan S, Eheart JW, Grarrett JH (1993) Neural network based screening for groundwater reclamation uncertainty. J Water Resour Res 29:563–574

    Article  Google Scholar 

  • Rogers LL, Dowla FU (1994) Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. J Water Resour Res 30:457–481

    Article  Google Scholar 

  • Scholkopf B, Sung KK (1997) Comparing support vector machine with Gaussian kernels to radial basis function classifiers. IEEE Trans Signal Process 45:2758–2765

    Article  Google Scholar 

  • Young GK (1967) Finding reservoir operating rules. J Hydraul Div ASCE 93(HY6):297–319

    Google Scholar 

  • Yu DL, Gomm JB, Williams D (1997) A recursive orthogonal least squares algorithm for training RBF networks. Neural Process Lett 5(3):167–176

    Article  Google Scholar 

  • Zhang B, Govindaraju RS (2000) Prediction of watershed runoff using Bayesian concepts and modular neural networks. J Water Resour Res 36:753–762

    Article  Google Scholar 

Download references

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Correspondence to Jian-xia Chang.

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Wang, Ym., Chang, Jx. & Huang, Q. Simulation with RBF Neural Network Model for Reservoir Operation Rules. Water Resour Manage 24, 2597–2610 (2010). https://doi.org/10.1007/s11269-009-9569-0

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  • DOI: https://doi.org/10.1007/s11269-009-9569-0

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