# Pore Water Pressure Prediction Based on Machine Learning Methods--Application to an Earth Dam Case
## 5 Conclusions  
In this paper, four different ML methods have been applied to build the PWP prediction models. It has been highlighted that both the static and dynamic neuron networks have a good ability (R\({}^{2}\) = 0.99) to predict the monitoring PWP in an earth dam. The findings of this study suggest that among four different ML methods, the MLP and RF-SA are more intelligent and accurate for predicting the PWP response. GRUs have a better prediction performance than RNNs in dynamic neural networks. Because the monitoring frequency of PWP in this study case is not enough for dynamic neural networks to study the features accurately, the accuracy of RNNs (which is appropriate for time-series prediction) is worse than that of the static ML models. Therefore, for different case studies, an attempt should be conducted to compare different ML approaches in order to select the best prediction method. In summary, the machine learning methods have some significant advantages in predicting the PWP of hydraulic response. In this case study, early decade measurements are still affected by the PWP dissipation process from the dam construction, and this aspect is well-reproduced by the different models used in this paper. These models can also demonstrate the soil's non-linear properties. The models established by ML methods are very flexible and do not need additional physical assumptions. They can be used in different earth dam positions, even in different dams. The architecture of the RF model could be optimized by the SA method efficiently, and the R\({}^{2}\) could be improved from 0.92 to 0.98. The architectures of ANN models, including MLP, RNNs, and GRU, could be optimized by the Adam method and loop algorithm. Figure 10: Loss function (defined by MSE) convergence processing in (**a**) RNNs and (**b**) GRU. The optimizing processes reveal that different batch sizes and epochs do not affect model performance as significantly as different numbers of hidden layers, learning rates, and neurons. The sensitivity analysis of these hyperparameters provides a reference for the tuning of ANNs for other machine learning applicators. By optimizing the hyperparameters (especially the value of hidden layers and neurons), the training results of ANNs can be greatly improved. The weaknesses of ML methods should also be noted. These methods rely on a large number of training datasets in which the input and output should be highly correlated. Most of the study cases do not have enough datasets, and there is a large uncertainty in the influence of the actual measured data. Therefore, an adequate amount of data is the basis for successful modeling in this study case. Conceptualization, C.C. and L.P.; methodology, L.A. and D.D. ; software, L.A.; validation, L.A., D.D., C.C., L.P. and P.B. ; formal analysis, L.A.; investigation, L.A. and X.G. ; resources, C.C. and L.P.; data curation, L.A. and C.C. ; writing--original draft preparation, L.A.; writing--review and editing, L.A., D.D., C.C., P.B. and O.J. ; visualization, L.A.; supervision, D.D., C.C., L.P., P.B. and O.J. ; project administration, D.D., L.P., and P.B. ; funding acquisition, D.D. and L.P. All authors have read and agreed to the published version of the manuscript. The China Scholarship Council provided the first author with a PhD scholarship for his research work (scholarship No. 202006430012). Apart from this scholarship, this research received no external funding. Not applicable. The data used in this study were provided by the company managing the dam studied. Restrictions apply to the availability of these data, which were used under license for this study. The data can be made available on request with the permission of this company. The first author gratefully thanks the China Scholarship Council (No. 202006430012) for providing her with a Ph.D. Scholarship for her research work. The authors would like to thank [PERSON] and [PERSON] (SMDEA) for providing the raw data from the dam studied. The authors declare no conflicts of interest.