The interaction between permafrost and atmosphere is accomplished through transfer of heat and moisture in the overlay active layer. Thus, the research on the thermal and hydrodynamics of active layer during the thawi...The interaction between permafrost and atmosphere is accomplished through transfer of heat and moisture in the overlay active layer. Thus, the research on the thermal and hydrodynamics of active layer during the thawing and freezing processes was considered a key to revealing the heat and moisture exchanges between permafrost and atmosphere. The monitoring and research on active layer were conducted because permafrost occupies about two thirds of the total area of the Tibetan Plateau. Based on the analysis of the ground temperature data and soil moisture data of monitoring near the Wudaoliang region of the Tibetan Plateau, the thawing and freezing processes of active layer were divided into four stages, i.e. summer thawing stage (ST), autumn freezing stage (AF), winter cooling stage (WC) and spring warming stage (SW). Coupled heat and water flow is much more complicated in ST and AF, and more amount of water is migrating in these two stages. Heat is transferred mainly via conductive heat flow in the展开更多
Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicabilit...Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicability and advantages of recurrent neural networks(RNNs)on PWP prediction,three variants of RNNs,i.e.,standard RNN,long short-term memory(LSTM)and gated recurrent unit(GRU)are adopted and compared with a traditional static artificial neural network(ANN),i.e.,multi-layer perceptron(MLP).Measurements of rainfall and PWP of representative piezometers from a fully instrumented natural slope in Hong Kong are used to establish the prediction models.The coefficient of determination(R^2)and root mean square error(RMSE)are used for model evaluations.The influence of input time series length on the model performance is investigated.The results reveal that MLP can provide acceptable performance but is not robust.The uncertainty bounds of RMSE of the MLP model range from 0.24 kPa to 1.12 k Pa for the selected two piezometers.The standard RNN can perform better but the robustness is slightly affected when there are significant time lags between PWP changes and rainfall.The GRU and LSTM models can provide more precise and robust predictions than the standard RNN.The effects of the hidden layer structure and the dropout technique are investigated.The single-layer GRU is accurate enough for PWP prediction,whereas a double-layer GRU brings extra time cost with little accuracy improvement.The dropout technique is essential to overfitting prevention and improvement of accuracy.展开更多
文摘The interaction between permafrost and atmosphere is accomplished through transfer of heat and moisture in the overlay active layer. Thus, the research on the thermal and hydrodynamics of active layer during the thawing and freezing processes was considered a key to revealing the heat and moisture exchanges between permafrost and atmosphere. The monitoring and research on active layer were conducted because permafrost occupies about two thirds of the total area of the Tibetan Plateau. Based on the analysis of the ground temperature data and soil moisture data of monitoring near the Wudaoliang region of the Tibetan Plateau, the thawing and freezing processes of active layer were divided into four stages, i.e. summer thawing stage (ST), autumn freezing stage (AF), winter cooling stage (WC) and spring warming stage (SW). Coupled heat and water flow is much more complicated in ST and AF, and more amount of water is migrating in these two stages. Heat is transferred mainly via conductive heat flow in the
基金supported by the Natural Science Foundation of China(Grant Nos.51979158,51639008,51679135,and 51422905)the Program of Shanghai Academic Research Leader by Science and Technology Commission of Shanghai Municipality(Project No.19XD1421900)。
文摘Knowledge of pore-water pressure(PWP)variation is fundamental for slope stability.A precise prediction of PWP is difficult due to complex physical mechanisms and in situ natural variability.To explore the applicability and advantages of recurrent neural networks(RNNs)on PWP prediction,three variants of RNNs,i.e.,standard RNN,long short-term memory(LSTM)and gated recurrent unit(GRU)are adopted and compared with a traditional static artificial neural network(ANN),i.e.,multi-layer perceptron(MLP).Measurements of rainfall and PWP of representative piezometers from a fully instrumented natural slope in Hong Kong are used to establish the prediction models.The coefficient of determination(R^2)and root mean square error(RMSE)are used for model evaluations.The influence of input time series length on the model performance is investigated.The results reveal that MLP can provide acceptable performance but is not robust.The uncertainty bounds of RMSE of the MLP model range from 0.24 kPa to 1.12 k Pa for the selected two piezometers.The standard RNN can perform better but the robustness is slightly affected when there are significant time lags between PWP changes and rainfall.The GRU and LSTM models can provide more precise and robust predictions than the standard RNN.The effects of the hidden layer structure and the dropout technique are investigated.The single-layer GRU is accurate enough for PWP prediction,whereas a double-layer GRU brings extra time cost with little accuracy improvement.The dropout technique is essential to overfitting prevention and improvement of accuracy.
