## 3 The Bayesian Updating Procedure of Monitor Data in the Large-Scale Landslide Model Experiment  
In this paper, the above-mentioned Bayesian updating method is used to calculate the SWCC model parameters. Bayesian updating is a method of statistical inference, in which as more evidence or information (in this paper is the observation samples) is obtained, [PERSON] theorem (Equation (2)) is used to update the probability of a hypothesis. First, the prior distribution of the SWCC model parameters is determined through reference research data or data from a SWCC experiment, as done in this work. Then, one uses the M-H method to generate a defined (large) number of random MCMC samples following a iterative calculation until \(\beta_{j}\) = 1, and the posterior distribution is obtained. The M-H method is a powerful Markov chain method with which to simulate multivariate distributions. The main steps include initialization and iteration [34]. Figure 2 shows a flowchart for implementation of the proposed Bayesian approach schematically. The work done covers both the experimental results to feed the model (left branch on Figure 2), and the analysis methodology (right branch on Figure 2). The experimental data were obtained after basic soil mechanical experiments, to characterize the material of the landslide body, and a large-scale landslide model experiment, including water content sensors to measure in real-time the hydrodynamic process. The setup and results of the experiments are described in Sections 4 and 5. Each step and its detailed description are summarized as follows. 1. Regarding the left branch, the main goal was to obtain Bayesian updating evidence (water content observation samples \(X_{obs}\)) through the water content detection of large-scale landslide models: 1. [label=()] 2. Infiltration experiments and a triaxial experiment were carried out, and the material ratio of landslide body was determined to be 7:3 for soil:stone through these experiments. 2. Large-scale landslide models were built according to the layer-by-layer compaction method. The place where the sensor was buried was carefully compacted to avoid damage to the sensor. 3. After the landslide model and sensor were ready, the moisture content sensor was tested to determine the reliability of its data. 2. In the right branch, the main work was to obtain the Bayesian updating simulation samples (\(X_{k}\)) through the finite element model: 1. [label=()] 2. The SWCC model was selected (VG model), and we analyzed the two model parameters within its range to observe their influences on the curve shape. 3. We assumed the prior distribution as normal or lognormal according to existing research. Then we determined the prior distribution of SWCC for the model parameters \(a\) and \(n\), based on experimental data and research results. 4. A finite element (FE) numerical model was developed according to the large-scale landslide test; we evaluated the seepage flow under the conditions of the reservoir water level. 5. In the Bayesian updating process, it is necessary to call the FE model, taking a long time to obtain the large number of output values. Therefore, this paper used an artificial neural network (ANN) as a surrogate model to speed up the analysis. 3. In the lower part of the flowchart, the main work was to perform Bayesian updating by combining the water content observation samples (\(X_{obs}\)) of the large landslide model of the left branch and the simulation samples (\(X_{k}\)) of the right branch: 1. [label=()] 2. The MCMC algorithm was used to generate \(N\) random samples that followed the posterior distribution of SWCC parameters and iteratively continued until \(\beta_{j}=1\). We got the posterior distribution of SWCC model parameters (posterior most likely value, posterior mean, posterior standard deviation, etc.). 3. We analyzed the factors influencing the posterior distribution results, such as the number of observation datums, the prior distribution, and the observation data that changed over time. 4.