IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v36y2022i8d10.1007_s11269-022-03161-w.html
   My bibliography  Save this article

Monitoring Inflow Dynamics in a Multipurpose Dam Based on Travel-time Principle

Author

Listed:
  • Mohamad Basel Sawaf

    (Hiroshima University)

  • Kiyosi Kawanisi

    (Hiroshima University)

  • Cong Xiao

    (Hiroshima University)

  • Gillang Noor Nugrahaning Gusti

    (Hiroshima University)

  • Faruq Khadami

    (Hiroshima University)

Abstract

Understanding inflow dynamics in a dam lake forms the basis for optimal dam operation and management practices. However, methods pertaining to adequately determining negative inflows and addressing them, as well as quantifying uncertainties in dam inflow, have been scarcely investigated. In this study, the inflow was observed using two pairs of fluvial acoustic tomography (FAT) systems placed diagonally in a dam lake, forming a crossed-shaped pattern. The “travel-time” principle is the primary approach for measuring the inflow by FAT. The novelty of this study is in discussing the inflow characteristics within a slow water-flow environment monitored by FAT. Based on the reciprocal sound transmission, we upgraded an equation to estimate the flow direction; this newly proposed generalized equation can be used in a fluctuating flow environment. We also discussed the sound propagation characteristics for slow flow velocities. Finally, we demonstrated that a small inaccuracy in the acoustic signal, even by a sub-millisecond, can cause significant errors in measurements. One of the novel findings of this study is the detection of internal waves using the improved flow direction equation and acoustic travel-time records. Overall, this study presents a promising approach for inflow measurements under extremely slow flow conditions.

Suggested Citation

  • Mohamad Basel Sawaf & Kiyosi Kawanisi & Cong Xiao & Gillang Noor Nugrahaning Gusti & Faruq Khadami, 2022. "Monitoring Inflow Dynamics in a Multipurpose Dam Based on Travel-time Principle," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2589-2610, June.
    • Handle: RePEc:spr:waterr:v:36:y:2022:i:8:d:10.1007_s11269-022-03161-w
    DOI: 10.1007/s11269-022-03161-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-022-03161-w
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-022-03161-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Parisa Noorbeh & Abbas Roozbahani & Hamid Kardan Moghaddam, 2020. "Annual and Monthly Dam Inflow Prediction Using Bayesian Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(9), pages 2933-2951, July.
    2. Mohamad Basel Al Sawaf & Kiyosi Kawanisi & Cong Xiao, 2020. "Measuring Low Flowrates of a Shallow Mountainous River Within Restricted Site Conditions and the Characteristics of Acoustic Arrival Times Within Low Flows," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3059-3078, August.
    Full references (including those not matched with items on IDEAS)

    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. Prabal Das & D. A. Sachindra & Kironmala Chanda, 2022. "Machine Learning-Based Rainfall Forecasting with Multiple Non-Linear Feature Selection Algorithms," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 6043-6071, December.
    2. Hamid Kardan Moghaddam & Mohammad Ebrahim Banihabib & Saman Javadi & Timothy O. Randhir, 2021. "A framework for the assessment of qualitative and quantitative sustainable development of groundwater system," Sustainable Development, John Wiley & Sons, Ltd., vol. 29(6), pages 1096-1110, November.
    3. Bao-Jian Li & Guo-Liang Sun & Yan Liu & Wen-Chuan Wang & Xu-Dong Huang, 2022. "Monthly Runoff Forecasting Using Variational Mode Decomposition Coupled with Gray Wolf Optimizer-Based Long Short-term Memory Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(6), pages 2095-2115, April.
    4. Atiyeh Bozorgi & Abbas Roozbahani & Seied Mehdy Hashemy Shahdany & Rouzbeh Abbassi, 2021. "Development of Multi-Hazard Risk Assessment Model for Agricultural Water Supply and Distribution Systems Using Bayesian Network," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(10), pages 3139-3159, August.
    5. Sarmad Dashti Latif & Ali Najah Ahmed & Edlic Sathiamurthy & Yuk Feng Huang & Ahmed El-Shafie, 2021. "Evaluation of deep learning algorithm for inflow forecasting: a case study of Durian Tunggal Reservoir, Peninsular Malaysia," 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. 109(1), pages 351-369, October.
    6. Javad Shafiee Neyestanak & Abbas Roozbahani, 2021. "Comprehensive Risk Assessment of Urban Wastewater Reuse in Water Supply Alternatives Using Hybrid Bayesian Network Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(14), pages 5049-5072, November.
    7. Hossien Riahi-Madvar & Majid Dehghani & Rasoul Memarzadeh & Bahram Gharabaghi, 2021. "Short to Long-Term Forecasting of River Flows by Heuristic Optimization Algorithms Hybridized with ANFIS," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(4), pages 1149-1166, March.
    8. M. Rajesh & Sachdeva Anishka & Pansari Satyam Viksit & Srivastav Arohi & S. Rehana, 2023. "Improving Short-range Reservoir Inflow Forecasts with Machine Learning Model Combination," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(1), pages 75-90, January.
    9. Milica Markovic & Jelena Markovic Brankovic & Miona Andrejevic Stosovic & Srdjan Zivkovic & Bojan Brankovic, 2021. "A New Method for Pore Pressure Prediction on Malfunctioning Cells Using Artificial Neural Networks," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(3), pages 979-992, February.
    10. Mojtaba Zangeneh & Mahdi Sarai Tabrizi & Amir Khosrojerdi & Ali Saremi, 2023. "Developing a decision-making model for improving the groundwater balance to control land subsidence," Soil and Water Research, Czech Academy of Agricultural Sciences, vol. 18(1), pages 55-65.
    11. Wentong Hu & Wenquan Gu & Donghao Miao & Dongguo Shao, 2022. "Research on the Ecological Flow and Water Replenishment Thresholds for Diversion Rivers Based on the MC-LOR Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(14), pages 5353-5369, November.

    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:spr:waterr:v:36:y:2022:i:8:d:10.1007_s11269-022-03161-w. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.