(HE Lu, WANG Shijun, GU Yanchang, et al. Prediction of seepage water level extremum of earth rock dam[J]. Hydro-Science and Engineering, 2021(4): 29-35. (in Chinese)). doi: 10.12170/20200824002
Citation: (HE Lu, WANG Shijun, GU Yanchang, et al. Prediction of seepage water level extremum of earth rock dam[J]. Hydro-Science and Engineering, 2021(4): 29-35. (in Chinese)). doi: 10.12170/20200824002

Prediction of seepage water level extremum of earth rock dam

  • Prediction of seepage water level extremum is one of the main means to monitor the safety of the earth rock dam, and dam body seepage water level is an important physical quantity to evaluate the seepage characteristics of the earth rock dam. Currently, the common sense of the extreme value prediction methods is on the prerequisite of giving correct independent variables, when such methods being applied. The most important factor affecting the seepage water level extremum is the upstream water level. When the correlation between the seepage water level extremum and the upstream water level is quite good, the predication accuracy provided by conventional models is quite high; when the correlation between the seepage water level extremum and the upstream water level is weak, the predication accuracy provided by conventional models is low. To resolve this issue, this paper proposes a method of predicting and evaluating the seepage water level extremum, which considers the measured value sequence only and neglects the independent variables. Based on the maximum Lyapunov index, a prediction model is established, and the ergodicity and stationary distribution of Markov chain are applied to evaluate this independent variable model. The example shows that the prediction effect of the prediction model based on maximum Lyapunov index is better than that of conventional methods for seepage water level extremum, which has weak correlation with independent variables, and the error assessment model based on Markov chain provides reasonable evaluation. The prediction model and evaluation method based on chaos theory and stochastic process that is proposed in this paper can form a systematic approach to the sequence prediction as well as evaluation method with high accuracy and strong practicability, covering the weak area of conventional prediction methods. It can be used to establish the prediction model for the measured value sequence with uncertain independent variables.
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