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<p>Rapid technological advances, agricultural expansion, and population growth ratio have accelerated the depletion of limited water resources, leading many countries, including Turkey, to emphasize the construction and use of underground dams as an effective strategy for sustainable water management. In order to contribute to the sustainability of underground dams, this study takes the Bahçecik (Bartın) Underground Dam as a case study, aiming to estimate the streamflow data required for the artificial recharge of underground reservoirs using surfacewater through wells. In this context, the streamflow of the main tributary recharging the dam was estimated by jointly evaluating machine learning techniques and hydrological basin modeling results. Time Series Analysis, Artificial Neural Networks (ANN), Multiple Linear Regression (MLR), and the similar basin area ratio methods used at the study. Time Series Analysis yielded Mean Absolute Percentage Error (MAPE) values ranging from 0.086 to 13.969%. The ANN method demonstrated superior performance in flow estimation at the E13A031 gauging station, achieving a coefficient of determination (𝑅²) of 0.802, while an 𝑅² value of 0.88 was obtained for the 2018 flow estimation of the Ovacuma Stream. These results underscore the effectiveness of integrating hydrological investigations with machine learning approaches in supporting sustainable water resource management.</p>

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Streamflow estimation for underground dams using machine learning and hydrological modeling: a case study of Bartın Bahçecik underground dam

  • 期刊论文

  • 发表日期：

    2025年9月1日

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  • Tülay Ekemen Keskin,
  • Emrah Şander

摘要

Rapid technological advances, agricultural expansion, and population growth ratio have accelerated the depletion of limited water resources, leading many countries, including Turkey, to emphasize the construction and use of underground dams as an effective strategy for sustainable water management. In order to contribute to the sustainability of underground dams, this study takes the Bahçecik (Bartın) Underground Dam as a case study, aiming to estimate the streamflow data required for the artificial recharge of underground reservoirs using surfacewater through wells. In this context, the streamflow of the main tributary recharging the dam was estimated by jointly evaluating machine learning techniques and hydrological basin modeling results. Time Series Analysis, Artificial Neural Networks (ANN), Multiple Linear Regression (MLR), and the similar basin area ratio methods used at the study. Time Series Analysis yielded Mean Absolute Percentage Error (MAPE) values ranging from 0.086 to 13.969%. The ANN method demonstrated superior performance in flow estimation at the E13A031 gauging station, achieving a coefficient of determination (𝑅²) of 0.802, while an 𝑅² value of 0.88 was obtained for the 2018 flow estimation of the Ovacuma Stream. These results underscore the effectiveness of integrating hydrological investigations with machine learning approaches in supporting sustainable water resource management.