## 5 Discussion
### Performance of VS and ER Models
\begin{tabular}{c c c c c c c} \hline \hline
**Statistics and Equation 1** & **Definition, Range, and Match** & \multicolumn{2}{c}{**Site 1**} & \multicolumn{2}{c}{**Site 2**} & \multicolumn{2}{c}{**Site 3**} \\ \cline{3-7}  & **VS** & **ER** & **ER** & **VS** & **ER** \\ \hline CVMAE: normalized MAE & Perfect match is 0. Acceptable match is \textless{}0.3. & 0.01 & 0.04 & 0.04 & 0.01 & 0.04 \\ \(=\frac{\text{MAE}}{\text{D}_{n}}\) & Perfect match is 0. Acceptable match is \textless{}0.3. & 0.07 & 0.06 & 0.06 & 0.07 & 0.09 \\ \hline CVSTD: normalized STD & Perfect match is 0. Acceptable match is \textless{}0.3. & 0.01 & 0.06 & 0.05 & 0.01 & 0.08 \\ \(=\frac{\text{SD}_{n}}{\text{D}_{n}}\) & Perfect match is 0. Acceptable match is \textless{}0.3. & 0.01 & 0.06 & 0.05 & 0.01 & 0.08 \\ \hline \(n\) & & 240 & 63 & 51 & 320 & 81 \\ \hline \(\text{MIN}_{m}\) & & 102.00 & 15.28 & 29.04 & 113 & 6.33 \\ \hline \(\text{MIN}_{p}\) & & 79.25 & 15.85 & 30.12 & 80.01 & 75.90 \\ \hline \(\text{MAX}_{m}\) & & 948.00 & 98.17 & 51.93 & 831.00 & 6.48 \\ \hline \(\text{MAX}_{p}\) & & 1015.61 & 96.88 & 49.48 & 867.02 & 72.24 \\ \hline \(\overline{D}_{m}\) & & 236.67 & 33.11 & 37.61 & 192.46 & 24.73 \\ \hline \(\overline{D}_{p}\) & & 237.60 & 32.82 & 36.64 & 190.03 & 24.57 \\ \hline \hline \end{tabular}   
* \({}^{1}\) Over the exploration space (a given geophysical profile), \(n\) is total number of data, \(\text{MIN}_{m}\) and \(\text{MAX}_{m}\) are minimum and maximum measured (0d) values, \(\text{MIN}_{m}\) and \(\text{MAX}_{m}\) are minimum and maximum predicted (\(p\)) values, and \(\overline{D}_{m}\) and \(\overline{D}_{p}\) are mean of the M and P data sets. \(D_{m}\) and \(D_{p}  
Performance of VS and ER models for geological model conceptualization, aimed at supporting groundwater flow modeling, departs from our initial ability to identify and segregate the influence of possible outliers caused by natural heterogeneity of variables VS and ER. As described in Section 4.2, VS is site-specific steady, whereas ER is site-specific transient, the latter varying over time and requiring periodic monitoring. For this reason, frequency of time-lapse ERT surveying and groundwater monitoring was adapted to the particular groundwater quantity (flow) and quality (EC) temporal dynamics of aquifer H; see Section 4.1. The evaluation of this optimal surveying frequency is crucial for the production of accurate geological models. Performance statistics of VS and ER models show satisfactory or very satisfactory matches between M and P data sets (Table 7), i.e., P data are less biased than their M counterparts, the relative difference being in the positive 0.01-0.02 range. Performance of the VS models is somewhat greater than of the ER models (Table 7). This performance analysis provides two important remarks: (1) theoretical fitting functions do not introduce noticeable mismatching between M and P data, i.e., do not generate spurious values, because the number and magnitude of original outliers is low to negligible; and (2) computation and fitting processes reproduce well the homogeneity of this alluvial medium, in which a general predictable behavior of variables VS and ER in non-surveyed sites can be anticipated from the results obtained in the surveyed ones, as show in next Section 5.2. Performance of the VS and ER models was better to that reported in similar experiences using the same methodology for MASW [59; 63; 64] and ERT [71; 72; 73]. As deduced, the VS and ER models are reliable enough to produce a confident geological model conceptualization for groundwater modeling purposes. As introduced in Section 1, errors in data acquisition for aquifer conceptualization are rarely segregated from the overall uncertainty appraisal of groundwater modeling tools. Aquifer conceptualization includes three stages subjected to error: (1) geometry definition, (2) acquisition of hydraulic data, and (3) evaluation of water balance components [15; 16; 17].