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Use of Recurrent Neural Network with Long Short-Term Memory for Seepage Prediction at Tarbela Dam, KP, Pakistan

Author

Listed:
  • Muhammad Ishfaque

    (Key Laboratory of Metallogenic Prediction of Nonferrous Metal of the Ministry of Education, School of Geoscience, and Info-Physics, Central South University, Changsha 410083, China
    Key Laboratory of Non-Ferrous Resources and Geological Hazard Detection, Central South University, Changsha 410083, China)

  • Qianwei Dai

    (Key Laboratory of Metallogenic Prediction of Nonferrous Metal of the Ministry of Education, School of Geoscience, and Info-Physics, Central South University, Changsha 410083, China
    Key Laboratory of Non-Ferrous Resources and Geological Hazard Detection, Central South University, Changsha 410083, China)

  • Nuhman ul Haq

    (Department of Computer Science, Comsat University Islamabad, Abbottabad Campus, Abbottabad 22060, Pakistan)

  • Khanzaib Jadoon

    (Department of Civil Engineering, Islamic International University, Islamabad 44000, Pakistan)

  • Syed Muzyan Shahzad

    (School of Geoscience, and Info-Physics, Central South University, Changsha 410083, China)

  • Hammad Tariq Janjuhah

    (Department of Geology, Shaheed Benazir Bhutto University, Dir (U), Sheringal 18050, Pakistan)

Abstract

Estimating the quantity of seepage through the foundation and body of a dam using proper health and safety monitoring is critical to the effective management of disaster risk in a reservoir downstream of the dam. In this study, a deep learning model was constructed to predict the extent of seepage through Pakistan’s Tarbela dam, the world’s second largest clay and rock dam. The dataset included hydro-climatological, geophysical, and engineering characteristics for peak-to-peak water inflows into the dam from 2014 to 2020. In addition, the data are time series, recurring neural networks (RNN), and long short-term memory (LSTM) as time series algorithms. The RNN–LSTM model has an average mean square error of 0.12, and a model performance of 0.9451, with minimal losses and high accuracy, resulting in the best-predicted dam seepage result. Damage was projected using a deep learning system that addressed the limitations of the model, the difficulties of calculating human activity schedules, and the need for a different set of input data to make good predictions.

Suggested Citation

  • Muhammad Ishfaque & Qianwei Dai & Nuhman ul Haq & Khanzaib Jadoon & Syed Muzyan Shahzad & Hammad Tariq Janjuhah, 2022. "Use of Recurrent Neural Network with Long Short-Term Memory for Seepage Prediction at Tarbela Dam, KP, Pakistan," Energies, MDPI, vol. 15(9), pages 1-16, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:9:p:3123-:d:801510
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    References listed on IDEAS

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