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Article Navigation >  CARSOLOGICA SINICA  > 2026  > Accepted Manuscript
FAN Zhujun, LIU Zhiwei, WANG Jiyang, ZHANG Yong, HUANG Qibo, LI Tengfang. Study on seepage leakage evaluation model of pumped storage power station in karst area[J]. CARSOLOGICA SINICA. doi: 10.11932/karst2026y008
Citation: FAN Zhujun, LIU Zhiwei, WANG Jiyang, ZHANG Yong, HUANG Qibo, LI Tengfang. Study on seepage leakage evaluation model of pumped storage power station in karst area[J]. CARSOLOGICA SINICA. doi: 10.11932/karst2026y008
shu

Study on seepage leakage evaluation model of pumped storage power station in karst area

doi: 10.11932/karst2026y008
  • 1.

    Northwest Electric Power Design Institute Co., LTD.Of China Power Engineering Consulting Group, Xian, Shaanxi710075, China

  • 2.

    Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR/International Research Center on Karst under the Auspices of UNESCO, Guilin, Guangxi 541004, China

  • Received Date: 2025-07-01
  • Accepted Date: 2025-12-15
  • Rev Recd Date: 2025-11-24
    • Available Online: 2026-03-31
    Abstract
  • So far, there is still no good method to quantitatively predict and evaluate the seepage volume of karst reservoirs.The reason is that the factors influencing the leakage of karst reservoirs are numerous and very complex, such as being affected by the lithology of the strata, the degree of structural development, the type of groundwater, the development of karst, hydrodynamic conditions, and anti-seepage treatment measures, etc. Most of these factors are not completely quantitative, and even random and ambiguous, making it difficult to describe them with deterministic models .On the other hand, the various factors influencing leakage have complex cross-effects and dynamic effects of mutual influence and mutual restraint. There exist complex nonlinear relationships among these factors, which are far beyond the description of a single (group of) simple algebraic equation.Machine learning algorithms have the characteristics of high accuracy and stability in simulating complex groundwater runoff and assessing reservoir seepage, which have attracted the interest of many researchers in recent years.Machine learning algorithms are a type of algorithm that can automatically analyze and obtain patterns from data and use these patterns to predict unknown data. They can automatically select the features with the highest correlation to evaluation events and solve problems such as noise, missing values, and outliers in the data, thereby improving the quality and integrity of the data.It has obvious advantages in reducing the time required for data processing. Compared with other classic statistical methods, its ability to identify nonlinear patterns of input and output is more reliable.Since most of the factors influencing reservoir leakage are qualitative and interact with each other, there is a certain correlation. Therefore, machine learning algorithms can be adopted to study the intrinsic connection and regularity between these qualitative influencing factors and the seepage of karst reservoirs, establish mathematical models, achieve the transformation from qualitative variables to quantitative evaluation, and conduct quantitative evaluation of the seepage volume of karst reservoirs.In this study, a model for predicting the leakage of karst reservoirs based on random forest (RF), artificial neural network (ANN), and vector machine (SVM) was innovatively constructed. By comparing and analyzing the results, it was found that all three models could achieve good prediction results. In this regard, the random forest model exhibited the best prediction performance: its simulated values were highly consistent with the measured values during the training and validation phases, accurately the dynamic change law of the leakage volume. The model has high prediction accuracy and good stability, and can be used as the preferred prediction model for the evaluation of reservoir leakage in karst areas. Four indicators, namely rock mass permeability, fault development degree, seepage channel morphology and hydrodynamic conditions, were selected as the main influencing factors of reservoir seepage. This research result provides new technical means and support for the risk assessment of leakage in pumped storage power stations.

     

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