TY - JOUR
T1 - Optimal water and waste load allocation in reservoir-river systems
T2 - A case study
AU - Nikoo, Mohammad Reza
AU - Kerachian, Reza
AU - Karimi, Akbar
AU - Azadnia, Ali Asghar
AU - Jafarzadegan, Keighobad
N1 - Funding Information:
Acknowledgments This study was financially supported by Islamic Azad University, East-Tehran Brach, Tehran, Iran.
PY - 2014/5
Y1 - 2014/5
N2 - In this paper, a new methodology is developed for optimization of water and waste load allocation in reservoir-river systems considering the existing uncertainties in reservoir inflow, waste loads and water demands. A stochastic dynamic programming (SDP) model is used to optimize reservoir operation considering the inflow uncertainty, and another model called PSO-SA is developed and linked with the SDP model for optimizing water and waste load allocation in downstream river. In the PSO-SA model, a particle swarm optimization technique with a dynamic penalty function for handling the constraints is used to optimize water and waste load allocation policies. Also, a simulated annealing technique is utilized for determining the upper and lower bounds of constraints and objective function considering the existing uncertainties. As the proposed water and waste load allocation model has a considerable run-time, some powerful soft computing techniques, namely, Regression tree Induction (named M5P), fuzzy K-nearest neighbor, Bayesian network, support vector regression and an adaptive neuro-fuzzy inference system, are trained and validated using the results of the proposed methodology to develop real-time water and waste load allocation rules. To examine the efficiency and applicability of the methodology, it is applied to the Dez reservoir-river system in the south-western part of Iran.
AB - In this paper, a new methodology is developed for optimization of water and waste load allocation in reservoir-river systems considering the existing uncertainties in reservoir inflow, waste loads and water demands. A stochastic dynamic programming (SDP) model is used to optimize reservoir operation considering the inflow uncertainty, and another model called PSO-SA is developed and linked with the SDP model for optimizing water and waste load allocation in downstream river. In the PSO-SA model, a particle swarm optimization technique with a dynamic penalty function for handling the constraints is used to optimize water and waste load allocation policies. Also, a simulated annealing technique is utilized for determining the upper and lower bounds of constraints and objective function considering the existing uncertainties. As the proposed water and waste load allocation model has a considerable run-time, some powerful soft computing techniques, namely, Regression tree Induction (named M5P), fuzzy K-nearest neighbor, Bayesian network, support vector regression and an adaptive neuro-fuzzy inference system, are trained and validated using the results of the proposed methodology to develop real-time water and waste load allocation rules. To examine the efficiency and applicability of the methodology, it is applied to the Dez reservoir-river system in the south-western part of Iran.
KW - Nonlinear interval optimization
KW - Operating rules
KW - Particle swarm optimization (PSO)
KW - Reservoir-river systems
KW - Simulated annealing (SA)
KW - Water and waste load allocation
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U2 - 10.1007/s12665-013-2801-5
DO - 10.1007/s12665-013-2801-5
M3 - Article
AN - SCOPUS:84899015993
SN - 1866-6280
VL - 71
SP - 4127
EP - 4142
JO - Environmental Earth Sciences
JF - Environmental Earth Sciences
IS - 9
ER -