Augmentation of an artificial neural network and modified stochastic dynamic programing model for optimal release policy

In this paper, a comprehensive modified stochastic dynamic programing with artificial neural network (MSDP-ANN) model is developed and applied to derive optimal operational strategies for a reservoir. Most water resource problems involve uncertainty. To show that the MSDP-ANN model addresses uncertainty in the input variable, the result of the MSDP-ANN model is compared with the performance of a detailed conventional stochastic dynamic programing with regression analysis (CSDP-RA) model. The computational time of the CSDP-ANN model is modified with concave objective functions by deriving a monotonic relationship between the reservoir storage and optimal release decision, and an algorithm is proposed to improve the computational efficiency of reservoir operation. Various indices (i.e. reliability, vulnerability, and resiliency) were calculated to assess the model performance. After comparing the performance of the CSDP-RA model with that of the MSDP-ANN model, it was observed that the MSDP-ANN model produces a more reliable and resilient model and a smaller supply deficit. Thus, it can be concluded that the MSDP-ANN model performs better than the CSDP-RA model in deriving the optimal operating policy for the reservoir.

[1]  Deepti Rani,et al.  Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation , 2010 .

[2]  Jery R. Stedinger,et al.  Reservoir optimization using sampling SDP with ensemble streamflow prediction (ESP) forecasts , 2001 .

[3]  Xudong Fu,et al.  Disaggregation Model of Daily Rainfall and Its Application in the Xiaolihe Watershed, Yellow River , 2010 .

[4]  Wei Li,et al.  Deriving Reservoir Refill Operating Rules by Using the Proposed DPNS Model , 2006 .

[5]  Ahmed El-Shafie,et al.  Performance of artificial neural network and regression techniques for simulation model in reservoir inter-relationships , 2011 .

[6]  J. J. Bogardi,et al.  Testing Stochastic Dynamic Programming Models Conditioned on Observed or Forecasted Inflows , 1991 .

[7]  Dionysia Panagoulia,et al.  Artificial neural networks and high and low flows in various climate regimes , 2006 .

[8]  Daniel P. Loucks,et al.  Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation , 1982 .

[9]  Jared L. Cohon,et al.  A Programming Model for Analysis of the Reliability, Resilience, and Vulnerability of a Water Supply Reservoir , 1986 .

[10]  Adebayo Adeloye,et al.  Artificial neural network based generalized storage–yield–reliability models using the Levenberg–Marquardt algorithm , 2006 .

[11]  Ximing Cai,et al.  Effect of streamflow forecast uncertainty on real-time reservoir operation , 2010 .

[12]  William W.-G. Yeh,et al.  Reservoir Management and Operations Models: A State‐of‐the‐Art Review , 1985 .

[13]  Fi-John Chang,et al.  Intelligent reservoir operation system based on evolving artificial neural networks , 2008 .

[14]  Sabah S. Fayaed,et al.  Reservoir-system simulation and optimization techniques , 2013, Stochastic Environmental Research and Risk Assessment.

[15]  Paulo Chaves,et al.  Deriving reservoir operational strategies considering water quantity and quality objectives by stochastic fuzzy neural networks , 2007 .

[16]  D. K. Srivastava,et al.  Application of ANN for Reservoir Inflow Prediction and Operation , 1999 .

[17]  V. Chandramouli,et al.  Deriving a General Operating Policy for Reservoirs Using Neural Network , 1996 .

[18]  Paulo Chaves,et al.  Stochastic Fuzzy Neural Network: Case Study of Optimal Reservoir Operation , 2007 .

[19]  Qiang Huang,et al.  Simulation with RBF Neural Network Model for Reservoir Operation Rules , 2010 .

[20]  John W. Labadie,et al.  Optimal Operation of Multireservoir Systems: State-of-the-Art Review , 2004 .

[21]  Avi Ostfeld,et al.  Implicit Mean-Variance Approach for Optimal Management of a Water Supply System under Uncertainty , 2013 .

[22]  Abbas Afshar,et al.  Fuzzy rule-based model for hydropower reservoirs operation , 2011 .

[23]  Fakhri Karray,et al.  Inferring operating rules for reservoir operations using fuzzy regression and ANFIS , 2007, Fuzzy Sets Syst..

[24]  Fakhri Karray,et al.  Reservoir Operation Using a Dynamic Programming Fuzzy Rule–Based Approach , 2005 .

[25]  Bing Chen,et al.  Field Investigation and Hydrological Modelling of a Subarctic Wetland - the Deer River Watershed , 2011 .

[26]  Ji Chen,et al.  Estimating irrigation water demand using an improved method and optimizing reservoir operation for water supply and hydropower generation: A case study of the Xinfengjiang reservoir in southern China , 2013 .

[27]  Alcigeimes B. Celeste,et al.  Evaluation of stochastic reservoir operation optimization models , 2009 .

[28]  Hamid R. Safavi,et al.  Optimal Reservoir Operation Based on Conjunctive Use of Surface Water and Groundwater Using Neuro-Fuzzy Systems , 2013, Water Resources Management.

[29]  Paresh Deka,et al.  Neural Network Based Decision Support Model for Optimal Reservoir Operation , 2005 .

[30]  Fakhri Karray,et al.  Minimizing variance of reservoir systems operations benefits using soft computing tools , 2003, Fuzzy Sets Syst..

[31]  Peter K. Kitanidis,et al.  Improved Dynamic Programming Methods for Optimal Control of Lumped-Parameter Stochastic Systems , 2001, Oper. Res..

[32]  Y. Chakrapani,et al.  Adaptive Neuro-Fuzzy Inference System based , 2009 .

[33]  V. Chandramouli,et al.  Multireservoir Modeling with Dynamic Programming and Neural Networks , 2001 .

[34]  A. R. Senthil kumar,et al.  Application of ANN, Fuzzy Logic and Decision Tree Algorithms for the Development of Reservoir Operating Rules , 2013, Water Resources Management.