Application of artificial intelligence models in the seepage flow prediction of dam: a case study of Shenzhen reservoir

Le résumé fourni par la source

Accurate seepage flow prediction is essential for risk assessment of earth-rockfill dams. This study incorporates convolutional neural network (CNN) with long short-term memory (LSTM), taking into account the advantages of both feature recognition and time series processing, and the CNN-LSTM model is first introduced to predict the dam seepage flow. Afterwards, the efficacy of the developed model is verified through nearly 20 years of monitoring data from two dams at Shenzhen Reservoir. Furthermore, another five well-developed models including support vector regression (SVR), adaptive boosting, artificial neural network, recurrent neural network, and LSTM models are also discussed. The hyperparametric optimisation of these six models is discussed as well. Results show that the type of kernel function significantly influences the accuracy of the SVR model, where the radial basis kernel function performs best. For the neural network model, more attention should be drawn to the choice of hidden nodes over other hyperparameters. In general, the prediction accuracy rankings of the five comparison models differ between the two dams, indicating that the model performance largely depends on the site characteristics. The CNN-LSTM model demonstrates consistently high predictive accuracy, suggesting its potential as a reliable tool for dam seepage flow forecasting.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.