Journal of Hydroinformatics Vol 10 No 4 pp 331–343 © IWA Publishing 2008 doi:10.2166/hydro.2008.057
Neural-network-based simulation-optimization model for water allocation planning at basin scale
M. Shourian, S. Jamshid Mousavi, M. B. Menhaj and E. Jabbari
School of Civil Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran E-mail: jmosavi@aut.ac.ir
School of Electrical Engineering, Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran
Department of Civil Engineering, Iran University of Science and Technology, Tehran, Iran
ABSTRACT
Heuristic search techniques are highly flexible, though they represent computationally intensive optimization methods that may require thousands of evaluations of expensive objective functions. This paper integrates MODSIM, a generalized river basin network flow model, a particle swarm optimization (PSO) algorithm and artificial neural networks into a modeling framework for optimum water allocations at basin scale. MODSIM is called in the PSO model to simulate a river basin system operation and to evaluate the fitness of each set of selected design and operational variables with respect to the model's objective function, which is the minimization of the system's design and operational cost. Since the direct incorporation of MODSIM into a PSO algorithm is computationally prohibitive, an ANN model as a meta-model is trained to approximate the MODSIM modeling tool. The resulting model is used in the problem of optimal design and operation of the upstream Sirvan river basin in Iran as a case study. The computational efficiency of the model makes it possible to analyze the model performance through changing its parameters so that better solutions are obtained compared to those of the original PSO–MODSIM model.
Keywords: ANNs; basin-wide water management; MODSIM; optimization; PSO; simulation
Full article (PDF Format)
PAY-PER-VIEW: Buy this article for £20.00 (IWA MEMBER PRICE: £15.00)
|
