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Application of artificial neural network for optimal operation of a multi-purpose multi-reservoir system, II: optimal solution and performance evaluation

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Abstract

This papers forms the second part of series on application of artificial neural network (ANN) for optimal operation of a multi-purpose multi-reservoir system. Optimal operating policies of a reservoir system are derived using Discrete differential dynamic programming (DDDP)-based ANN model. In ANN model development a feed-forward network with delta learning rule and back propagation algorithm is used. Neural networks have been trained using supervised learning approach. Water supply for irrigation, municipal and industrial use have been selected as objective of operation and other purposes are treated as binding constraints. Minimization of the sum of square of penalties incurred due to deviation of release from the target, is selected as the objective function. Damodar Valley (DV), a multi-purpose four reservoir system in India is used for this study. With different combination of input data, five types of ANN models are developed. Simulation has been done with 5 years (out of 1000 years) generated monthly inflow sequence as well as three types of observed historical monthly inflow sequences: maximum annual inflow year, 75% dependable inflow year and minimum annual inflow year. For simulation, total 360 monthly networks are trained and stored. ANN model: in which initial storage, current period’s inflow and previous period’s inflow are considered as input and optimal final state as output, yields lowest objective function value. Performances of the said model is computed based on modern reliability parameters, i.e., reliability, resiliency and vulnerability.

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References

  • ASCE task committee on application of artificial neural networks in hydrology [Rao Govindaraju] (2000a) I: preliminary concept. J Hydrol Eng 5(2):115–123

    Article  Google Scholar 

  • ASCE task committee on application of artificial neural networks in hydrology [Rao Govindaraju], (2000b) II: Hydrologic application. J Hydrol Eng 5(2):124–137

    Article  Google Scholar 

  • Bowden GJ, Dandy GC, Maier HR (2005) Input determination for neural network models in water resources application. Part I-background and methodology. J Hydrol 301(1–4):75–92

    Google Scholar 

  • Bozorg-Haddad O, Aboutalebi MA, Parisa-Sadat A, Hugo A, Loáiciga HA (2018) Real-time reservoir operation using data mining techniques. Environ Monit Assess 18(190):6970

    Google Scholar 

  • Cancelliere A, Giuliano G, Ancarani A, Rossi G (2002) A neural networks approach for deriving irrigation reservoir operating rules. Water Resour Manag 16(1):71–88

    Article  Google Scholar 

  • Chaves P, Chang FJ (2008) Intelligent reservoir operation system based on evolving artificial neural networks. Adv Water Resour 31(6):926–936

    Article  Google Scholar 

  • Chandramouli V, Deka P (2005) Neural network based decision support model for optimal reservoir operation. Water Resour Manag 19(4):447–464

    Article  Google Scholar 

  • Chandramouli V, Raman H (2001) Multireservoir modeling with dynamic programming and neural networks. J Water Resour Plan Manag Div Am Soc Civ Eng 127(2):89–98

    Article  Google Scholar 

  • Chandramouli V, Kuppusamy KA, Manikandan K (2002) Study on water sharing in a multi-reservoir system using dynamic programming and neural network model. J Water Resour Dev 18(3):425–438

    Article  Google Scholar 

  • Chen L, Chang FJ (2007) Applying real coded multi-population genetic algorithm to multireservoir operation. Hydrol Process 21(5):668–698

    Article  Google Scholar 

  • Chen L, Macphee J, Yeh WW (2007) A diversified multiobjective GA for optimizing reservoir rule curves. Adv Water Resour 30(5):1082–1093

    Article  Google Scholar 

  • Coerver HM, Ruten MM, van de Giesen NC (2018) Deduction of reservoir operating rules for application in global hydrological models. Hydrol Earth Syst Sci 22:831–851

    Article  Google Scholar 

  • Esmaeil-Beik S, Yu YS (1984) Optimal operation of multipurpose pool of Elk City Lake. J Water Resour Plan Manag Div Am Soc Civ Eng 110(1):1–14

    Article  Google Scholar 

  • Fayaed SS, El-Shafie A, Jaafar O (2013) Integrated artificial neural network and stochastic dynamic programming model for optimal release policy. Water Resour Manag 27(4):3679–3696

    Article  Google Scholar 

  • Hashimoto T, Stedinger JR, Loucks DP (1982) Reliability, resiliency, and vulnerability criteria for water resource system performance evaluation. Water Resour Res 18(1):14–20

    Article  Google Scholar 

  • Heidari M, Chow VT, Kokotovic PV, Meredith DD (1971) Discrete differential dynamic programming approach to water resources systems optimization. Water Resour Res 7(2):273–282

    Article  Google Scholar 

  • Huang W-C, Harboe R, Bogardi JJ (1991) Testing stochastic dynamic programming models conditioned on observed or forecasted inflows. J Water Resour Plan Manag Div Am Soc Civ Eng 117(1):28–36

    Article  Google Scholar 

  • Huang W-C, Yuan L-C, Lee C-M (2002) Linking genetic algorithms with stochastic dynamic programming to the long-term operation of a multi-reservoir system. Water Resour Res 38(12):4209–4216

    Article  Google Scholar 

  • Jain SK, Das A, Srivastava DK (1999) Application of ANN for reservoir inflow prediction and operation. J Water Resour Plann Manag Div Am Soc Civ Eng 125(5):263–271

    Article  Google Scholar 

  • Khare SS, Gajbhiye AR (2013) Application of artificial neural network (Ann) in operation of reservoirs. IOSR J Mech Civ Eng 6(1):16–21

    Article  Google Scholar 

  • Kim T, Heo JH (2006) Application of multiobjective genetic algorithms to multireservoir system optimization in the Han River basin. KSCE J Civil Eng 10(5):371–380

    Article  Google Scholar 

  • Kim T, Heo JH, Jeong CS (2006) Multireservoir system optimization in the Han River basin using multi-objective genetic algorithms. Hydrol Process 20(9):2057–2075

    Article  Google Scholar 

  • Loucks DP, Stedinger JR, Haith DA (1981) Water resources systems planning and analysis. Prentice-Hall, NJ

    Google Scholar 

  • Maier NJHR, Lence BJ, Tolson BA, Foschi RO (2001) First-order reliability method for estimating reliability, vulnerability, and resilience. Water Resour Res 37(3):779–790

    Article  Google Scholar 

  • Moy W-S, Cohon JL, ReVelle CS (1986) A programming model for analysis of the reliability, resilience, and vulnerability of a water supply reservoir. Water Resour Res 22(4):489–498

    Article  Google Scholar 

  • Raman H, Chandramouli V (1996) Deriving a general operating policy for reservoirs using neural network. J Water Resour Plann Manag Div Am Soc Civ Eng 122(5):342–347

    Article  Google Scholar 

  • Rani S, Parekh F (2014) Application of artificial neural network (ANN) for reservoir water level forcasting. Int J Sci Res 3(7):1077–1082

    Google Scholar 

  • Reddy M, Kumar D (2006) Optimal reservoir operation using multi-objective evolutionary algorithm. Water Resour Manag 20(6):861–878

    Article  Google Scholar 

  • Sangiorgio M, Guariso G (2018) NN-based implicit stochastic optimization of multi-reservoir systems management. Water 10:302–323

    Article  Google Scholar 

  • Sharif M, Wardlaw R (2000) Multireservoir system optimization using genetic algorithm: case study. J Comput Civil Eng ASCE 14:255–263

    Article  Google Scholar 

  • Sinha B, Rao PR (1985) A study for optimum utilization of the Damodar water resources. Sadhana 8(3):273–290

    Article  Google Scholar 

  • Su SY, Deininger RA (1972) Generalization of White’s method of successive approximations to periodic Markovian decision process. Oper Res 20(2):318–326

    Article  Google Scholar 

  • Tospornsampan J, Kita I, Ishii M, Kitamura Y (2005) Discrete differential dynamic programming and neural network on deriving a general operating policy of a multiple reservoir system: A case study in the Mae Klong System. Thai J Rainwater Catch Syst 11(1):1–9

    Article  Google Scholar 

  • Wang Y, Chang J, Huang Q (2010) Simulation with RBF neural network model for reservoir operation rules. Water Resour Manag 24(12):2597–2610

    Article  Google Scholar 

  • Wardlaw R, Sharif M (1999) Evaluation of genetic algorithms for optimal reservoir system operation. J Water Resour Plann Manag Div Am Soc Civ Eng 125(1):25–33

    Article  Google Scholar 

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  1. Department of Civil Engineering, Aliah University, Kolkata, West Bengal, India

    Safayat Ali Shaikh

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  1. Safayat Ali Shaikh
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Shaikh, S.A. Application of artificial neural network for optimal operation of a multi-purpose multi-reservoir system, II: optimal solution and performance evaluation. Sustain. Water Resour. Manag. 6, 66 (2020). https://doi.org/10.1007/s40899-020-00423-6

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