Hassan, Shahzal, Shaukat, Nadeem ORCID: 0000-0002-4655-0476, Ahmad, Ammar, Abid, Muhammad, Hashmi, Abrar ORCID: 0000-0002-8683-7965, Shahid, Muhammad Laiq Ur Rahman ORCID: 0000-0002-5223-4744, Rajabi, Zohreh ORCID: 0000-0002-7479-7652 and Tariq, Muhammad Atiq Ur Rehman ORCID: 0000-0002-0226-7310 (2022) Prediction of the amount of sediment Deposition in Tarbela Reservoir using machine learning approaches. Water (Switzerland), 14 (19). ISSN 2073-4441

Abstract

Tarbela is the largest earth-filled dam in Pakistan, used for both irrigation and power production. Tarbela has already lost around 41.2% of its water storage capacity through 2019, and WAPDA predicts that it will continue to lose storage capacity. If this issue is ignored for an extended period of time, which is not far away, a huge disaster will occur. Sedimentation is one of the significant elements that impact the Tarbela reservoir’s storage capacity. Therefore, it is crucial to accurately predict the sedimentation inside the Tarbela reservoir. In this paper, an Artificial Neural Network (ANN) architecture and multivariate regression technique are proposed to validate and predict the amount of sediment deposition inside the Tarbela reservoir. Four input parameters on yearly basis including rainfall (Ra), water inflow (Iw), minimum water reservoir level (Lr), and storage capacity of the reservoir (Cr) are used to evaluate the proposed machine learning models. Multivariate regression analysis is performed to undertake a parametric study for various combinations of influencing parameters. It was concluded that the proposed neural network model estimated the amount of sediment deposited inside the Tarbela reservoir more accurately as compared to the multivariate regression model because the maximum error in the case of the proposed neural network model was observed to be 4.01% whereas in the case of the multivariate regression model was observed to be 60.7%. Then, the validated neural network model was used for the prediction of the amount of sediment deposition inside the Tarbela reservoir for the next 20 years based on the time series univariate forecasting model ETS forecasted values of Ra, Iw, Lr, and Cr. It was also observed that the storage capacity of the Tarbela reservoir is the most influencing parameter in predicting the amount of sediment.

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