# Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks
## 6 ANN Models
### ANN Models for Predicting Water Content  
Water content is an important index property of soil. The amount of water gives an indication of grading of soil and porosity. Soil compaction and strength is highly affected by water content. ANN models are developed to predict water content using lab and field data independently and in combination. Based on the previous models for velocity this analysis is developed without validation. Models are developed with and without velocity as input to evaluate if velocity helps in predicting water content. The statistics for the six different models are presented in Table 5.  
The best model to predict water content from LL, plasticity index, and dry density using field data is Network 3_(_2\(3\)1000)_1. The predicted versus actual graph for the best network is shown in Figure 2a indicates that the accuracy of predicting water content is good. Predicting water content from field data by including seismic wave velocity \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source of Data** & **Model** & **Model Structure** & **ASE** & **MARE** & **R\({}^{2}\)** \\ \hline Field & with validation & 4_(_4\(4\)3100) \_1 & 0.0133 & 19.04 & 0.34 \\  & without validation & 4_(_2\(4\)2900) \_1 & 0.0128 & 19.34 & 0.37 \\  & with & & & & \\ Lab & validation & 4_(_2_200) \_1 & 0.0079 & 14.86 & 0.71 \\  & without validation & 4_(_1\(5\)100) \_1 & 0.0058 & 13.33 & 0.80 \\  & with & & & & \\ Field plus Lab & validation & 4_(_1\(2\)2900) \_1 & 0.0174 & 30.75 & 0.50 \\  & without validation & 4_(_5_10_20000) \_1 & 0.0075 & 18.53 & 0.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparing ANN models (predicting seismic wave velocity). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Source of Data** & **Model** & **Model Structure** & **ASE** & **MARE** & **R\({}^{2}\)** \\ \hline Field & without velocity & 3_(_2\(3\)1000) \_1 & 0.0087 & 10.68 & 0.75 \\  & with velocity & 4_(_1\(5\)100) \_1 & 0.0051 & 7.78 & 0.85 \\ Lab & velocity & 3_(_4\(4\)100) \_1 & 0.0051 & 7.69 & 0.82 \\  & with velocity & 4_(_3\(3\)100) \_1 & 0.0043 & 6.63 & 0.89 \\  & without velocity & 3_(_5_12_100) \_1 & 0.006 & 10.69 & 0.74 \\  & with velocity & 4_(_3\(3\)1000) \_1 & 0.0083 & 11.61 & 0.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparing ANN models (predicting water content). into the input parameters is Network 4_(1\(5\)100). The predicted versus actual graph is shown in Figure 2b and the statistics indicate better accuracy in predicting water content. So, the results indicate that including seismic wave velocity improves the prediction of water content. Using lab data to predict water content from LL, plasticity index, and dry density is best for network 3_(3\(3\)100) \(1\) The predicted versus actual graphs are shown in Figure 2c and statistics indicate that the accuracy of predicting moisture content is higher than the model derived from field data. Adding the velocity as additional input to traditional lab data results in network 4_(3\(3\)100) \(1\) with even better accuracy. The predicted versus  
Figure 2: Graphical prediction accuracy of model for predicting moisture content, (**a**) field, without velocity, (**b**) field, with velocity, (**c**) lab, without velocity, (**d**) lab, with velocity, (**e**) field plus lab, without velocity, (**f**) field plus lab, with velocity. actual graph for this network is shown in Figure 2d and the statistics shown in Table 5 indicate this is the best model for predicting moisture content. In summary, the models for predicting moisture content are better using laboratory data than field data and the addition of seismic velocity information enhances the models  
Combining lab and field data to develop ANN models to predict water content reduces the performance of the models. Network 3_(5_12_100)_1 is the best network without the velocity. The predicted versus actual graph, is shown in Figure 2e.