# Pore Water Pressure Prediction Based on Machine Learning Methods--Application to an Earth Dam Case
###### Abstract  
Pore water pressure (PWP) response is significant for evaluating the earth dams' stability, and PWPs are, therefore, generally monitored. However, due to the soil heterogeneity and its non-linear behavior within earths, the PWP is usually difficult to estimate and predict accurately in order to detect a pathology or anomaly in the behavior of an embankment dam. This study endeavors to tackle this challenge through the application of diverse machine learning (ML) techniques in estimating the PWP within an existing earth dam. The methods employed include random forest (RF) combined with simulated annealing (SA), multilayer perceptron (MLP), standard recurrent neural networks (RNNs), and gated recurrent unit (GRU). The prediction capability of these techniques was gauged using metrics such as the coefficient of determination (R\({}^{2}\)), mean square error (MSE), and CPU training time. It was found that all the considered ML methods could give satisfactory results for the PWP estimation. Upon comparing these methods within the case study, the findings suggest that, in this study, multilayer perceptron (MLP) gives the most accurate PWP prediction, achieving the highest coefficient of determination (R\({}^{2}\) = 0.99) and the lowest mean square error (MSE = 0.0087) metrics. A sensitivity analysis is then presented to evaluate the models' robustness and the hyperparameter's influence on the performance of the prediction model. earth dam; machine learning; artificial neural networks; pore water pressure; monitoring data 202  
in the behavior of the dam. For example, an increase in PWP that is not explained by a cyclic/reversible variation in the level of the reservoir or the season could be associated with a pathology, such as the clogging of the drainage system or the initiation of an internal erosion process. Initially, PWP prediction models were developed based on the fundamental laws of fluid mechanics in porous media [3]. Analytical solutions [4; 5], numerical simulations [6; 7; 8], statistic models [9; 10], and probabilistic models [11; 12] have been developed to obtain the PWP by applying these different methodologies. In practical engineering, due to inhomogeneous and highly non-linear phenomena in earth soils, these models could not accurately evaluate the hydraulic behavior by comparing the provided results with the in situ measurements. The trend toward automating dam monitoring devices is gaining momentum, enabling higher reading frequencies and an abundance of monitoring data [13]. Within the machine learning (ML) community, sophisticated tools have been developed for constructing data-driven prediction models in geotechnical engineering [14; 15] such as to evaluate soil properties [16], investigate the reliability of excavations [17; 18; 19], analyze the deformations [20; 21] and ground settlement [22], as well as predict geotechnical parameters [23]. The significant advantage of the ML methods is that they can fit highly non-linear behaviors. This is a key factor in their current popularity. There are several recent works available in the literature on the use of machine learning methods for the analysis of dam monitoring measurements. More specifically, [13; 24; 25; 26] are interested in modeling displacement measurements of arch dams; [27; 28] focus on displacements of gravity and buttress dams; [9; 29] deal with pore pressures at the base of arch dams, and [30] concern settlements of embankment dams. Advancements have been made in utilizing machine learning (ML) techniques for predicting pore water pressure (PWP) in diverse geotechnical fields like slope stability. [PERSON] et al. (2013) [31] investigated the effectiveness of different training algorithms within ANN for PWP prediction. Their study highlighted the Levenberg Marquardt (LM) algorithm as particularly efficient and rapid in modeling the dynamics of soil pore water pressure changes in response to rainfall patterns.