IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i19p5367-d271654.html
   My bibliography  Save this article

Improving Dam and Reservoir Operation Rules Using Stochastic Dynamic Programming and Artificial Neural Network Integration Model

Author

Listed:
  • Sabah Saadi Fayaed

    (Civil Engineering Department, Faculty of Engineering, Komar University of Science and Technology, Sulaymaniyah 00964, Iraq)

  • Seef Saadi Fiyadh

    (Nanotechnology & Catalysis Research Centre (NANOCAT), IPS Building, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Wong Jee Khai

    (Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Kajang 43000, Selangor, Malaysia)

  • Ali Najah Ahmed

    (Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Kajang 43000, Selangor, Malaysia
    Institute of Energy Infrastructure (IEI), Universiti Tenaga Nasional, Selangor 43000, Malaysia)

  • Haitham Abdulmohsin Afan

    (Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Rusul Khaleel Ibrahim

    (Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Chow Ming Fai

    (Department of Civil Engineering, College of Engineering, Universiti Tenaga Nasional (UNITEN), Kajang 43000, Selangor, Malaysia)

  • Suhana Koting

    (Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Nuruol Syuhadaa Mohd

    (Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Wan Zurina Binti Jaafar

    (Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Lai Sai Hin

    (Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

  • Ahmed El-Shafie

    (Department of Civil Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia)

Abstract

The simulation elevation-surface area-storage interrelationship of a reservoir is a crucial task in developing ideal water release policies for reservoir and dam operations. In this study, an inclusive (stochastic dynamic programming-artificial neural network (SDP-ANN)) model was established and applied to obtain an ideal reservoir operation strategy for Sg. Langat reservoir in Malaysia. The problems associated with the management of water resources mostly relate to uncertainty and the stochastic nature of the reservoir inflow, and the SDP-ANN model is meant to consider uncertainty in the input parameters such as reservoir inflow and reservoir evaporation losses. The performance of the SDP-ANN model was compared to that of the stochastic dynamic programming-autoregression (AR) model. The primary aim of the model is to decrease the squared deviation from the desired water release, which we determined by comparing the SDP-AR and SDP-ANN model performances. The results indicate that the SDP-ANN model demonstrated greater resilience and reliability with a lower supply deficit. Consequently, the case study results confirm that the SDP-ANN model performs better than the SDP-AR model in obtaining the best parameters for the reservoir operation. Specifically, a comparison of the models shows that the proposed Model 2 increased the reliability and resilience of the system by 7.5% and 6.3%, respectively.

Suggested Citation

  • Sabah Saadi Fayaed & Seef Saadi Fiyadh & Wong Jee Khai & Ali Najah Ahmed & Haitham Abdulmohsin Afan & Rusul Khaleel Ibrahim & Chow Ming Fai & Suhana Koting & Nuruol Syuhadaa Mohd & Wan Zurina Binti Ja, 2019. "Improving Dam and Reservoir Operation Rules Using Stochastic Dynamic Programming and Artificial Neural Network Integration Model," Sustainability, MDPI, vol. 11(19), pages 1-17, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5367-:d:271654
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/19/5367/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/19/5367/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. V. Jothiprakash & Ganesan Shanthi, 2006. "Single Reservoir Operating Policies Using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 20(6), pages 917-929, December.
    2. Ahmed El-Shafie & Mahmoud Taha & Aboelmagd Noureldin, 2007. "A neuro-fuzzy model for inflow forecasting of the Nile river at Aswan high dam," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 21(3), pages 533-556, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Efsun Bacaksız & Mücahit Opan & Zuhal Elif Kara Dilek & Murat Karadeniz, 2023. "Evaluation of Optimal Energy Productıon Usıng Deterministic, Probabilistic and Risky Cases In a Multi-Reservoir System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(15), pages 5829-5848, December.
    2. Suwapat Kosasaeng & Nirat Yamoat & Seyed Mohammad Ashrafi & Anongrit Kangrang, 2022. "Extracting Optimal Operation Rule Curves of Multi-Reservoir System Using Atom Search Optimization, Genetic Programming and Wind Driven Optimization," Sustainability, MDPI, vol. 14(23), pages 1-14, December.
    3. Sarmad Dashti Latif & Suzlyana Marhain & Md Shabbir Hossain & Ali Najah Ahmed & Mohsen Sherif & Ahmed Sefelnasr & Ahmed El-Shafie, 2021. "Optimizing the Operation Release Policy Using Charged System Search Algorithm: A Case Study of Klang Gates Dam, Malaysia," Sustainability, MDPI, vol. 13(11), pages 1-19, May.
    4. Dipsikha Devi & Anupal Baruah & Arup Kumar Sarma, 2022. "Characterization of dam-impacted flood hydrograph and its degree of severity as a potential hazard," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 112(3), pages 1989-2011, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Deepti Rani & Maria Moreira, 2010. "Simulation–Optimization Modeling: A Survey and Potential Application in Reservoir Systems Operation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(6), pages 1107-1138, April.
    2. T. Fowe & I. Nouiri & B. Ibrahim & H. Karambiri & J. Paturel, 2015. "OPTIWAM: An Intelligent Tool for Optimizing Irrigation Water Management in Coupled Reservoir–Groundwater Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3841-3861, August.
    3. Mojtaba Moravej & Seyed-Mohammad Hosseini-Moghari, 2016. "Large Scale Reservoirs System Operation Optimization: the Interior Search Algorithm (ISA) Approach," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(10), pages 3389-3407, August.
    4. Fang-Fang Li & Jia-Hua Wei & Xu-Dong Fu & Xin-Yu Wan, 2012. "An Effective Approach to Long-Term Optimal Operation of Large-Scale Reservoir Systems: Case Study of the Three Gorges System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(14), pages 4073-4090, November.
    5. Wensheng Wang & Juliang Jin & Yueqing Li, 2009. "Prediction of Inflow at Three Gorges Dam in Yangtze River with Wavelet Network Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(13), pages 2791-2803, October.
    6. K. Ramakrishnan & C. Suribabu & T. Neelakantan, 2010. "Crop Calendar Adjustment Study for Sathanur Irrigation System in India Using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(14), pages 3835-3851, November.
    7. Gokmen Tayfur, 2017. "Modern Optimization Methods in Water Resources Planning, Engineering and Management," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(10), pages 3205-3233, August.
    8. V. Jothiprakash & R. Arunkumar, 2013. "Optimization of Hydropower Reservoir Using Evolutionary Algorithms Coupled with Chaos," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(7), pages 1963-1979, May.
    9. Mohammad Azizipour & Vahid Ghalenoei & M. H. Afshar & S. S. Solis, 2016. "Optimal Operation of Hydropower Reservoir Systems Using Weed Optimization Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(11), pages 3995-4009, September.
    10. Dalibor Petković & Milan Gocic & Slavisa Trajkovic & Miloš Milovančević & Dragoljub Šević, 2017. "Precipitation concentration index management by adaptive neuro-fuzzy methodology," Climatic Change, Springer, vol. 141(4), pages 655-669, April.
    11. Qiang Zhang & Ben-De Wang & Bin He & Yong Peng & Ming-Lei Ren, 2011. "Singular Spectrum Analysis and ARIMA Hybrid Model for Annual Runoff Forecasting," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(11), pages 2683-2703, September.
    12. Jehangir Awan & Deg-Hyo Bae, 2014. "Improving ANFIS Based Model for Long-term Dam Inflow Prediction by Incorporating Monthly Rainfall Forecasts," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(5), pages 1185-1199, March.
    13. Arvin Samadi-koucheksaraee & Iman Ahmadianfar & Omid Bozorg-Haddad & Seyed Amin Asghari-pari, 2019. "Gradient Evolution Optimization Algorithm to Optimize Reservoir Operation Systems," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 603-625, January.
    14. Jatin Anand & Ashvani Kumar Gosain & Rakesh Khosa, 2018. "Optimisation of Multipurpose Reservoir Operation by Coupling Soil and Water Assessment Tool (SWAT) and Genetic Algorithm for Optimal Operating Policy (Case Study: Ganga River Basin)," Sustainability, MDPI, vol. 10(5), pages 1-20, May.
    15. V. Jothiprakash & Ganesan Shanthi & R. Arunkumar, 2011. "Development of Operational Policy for a Multi-reservoir System in India using Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(10), pages 2405-2423, August.
    16. Md. Hossain & A. El-shafie, 2013. "Intelligent Systems in Optimizing Reservoir Operation Policy: A Review," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 27(9), pages 3387-3407, July.
    17. Yahia Mutalib Tofiq & Sarmad Dashti Latif & Ali Najah Ahmed & Pavitra Kumar & Ahmed El-Shafie, 2022. "Optimized Model Inputs Selections for Enhancing River Streamflow Forecasting Accuracy Using Different Artificial Intelligence Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 5999-6016, December.
    18. Ahmad Khazaee Poul & Mojtaba Shourian & Hadi Ebrahimi, 2019. "A Comparative Study of MLR, KNN, ANN and ANFIS Models with Wavelet Transform in Monthly Stream Flow Prediction," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2907-2923, June.
    19. Muhammad Sulaiman & Ahmed El-Shafie & Othman Karim & Hassan Basri, 2011. "Improved Water Level Forecasting Performance by Using Optimal Steepness Coefficients in an Artificial Neural Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(10), pages 2525-2541, August.
    20. Hadi Sanikhani & Ozgur Kisi & Mohammad Nikpour & Yagob Dinpashoh, 2012. "Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4347-4365, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5367-:d:271654. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.