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

Muhammad Ishfaque, Qianwei Dai, Nuhman ul Haq, Khanzaib Jadoon, Syed Muzyan Shahzad and Hammad Tariq Janjuhah
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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
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
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

Energies, 2022, vol. 15, issue 9, 1-16

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.

Keywords: dam seepage; deep learning; recurrent neural network; LSTM; prediction; time series data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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