## 5 Final Remarks  
Within the context of applied sciences, we highlight that the core of the paper is to bring forward a method for automatically selecting the location of the control point (or control points) of geodetic networks based on the reliability theory. The proposed method to design a geodetic network is based on the smallest value of the maximum external reliability. The size of the outlier is defined according to MDB for a given type I and type  
Figure 6: Maximum external reliability max \((\
abla\mathbf{X})\) [mm] for two control points in the network. Figure 7: Optimum configuration of the network for two control points. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{8}{c}{**MDB (in \(\sigma\) _unit_)**} \\ \hline  & \multicolumn{3}{c}{**Observations with Same Variances**} & \multicolumn{3}{c}{**Observations with Different Variances**} \\ \cline{2-10}
**Control Point** & **Average** & **Max.** & **Min.** & **Std. Dev.** & **Average** & **Max.** & **Min.** & **Std. Dev.** \\ \hline AB & 5.41 & 5.41 & 5.41 & 0.00 & 5.52 & 6.39 & 4.69 & 0.67 \\ AC, AF, DB or BE & 5.64 & 6.56 & 5.1 & 0.50 & 5.86 & 7.83 & 4.49 & 1.16 \\ CD or EF & 5.69 & 6.45 & 4.98 & 0.63 & 5.93 & 7.5 & 4.6 & 1.28 \\ CG, DG, EG or FG & 5.69 & 6.56 & 5.00 & 0.65 & 5.97 & 7.85 & 4.57 & 1.35 \\ AD, AE, CB or BF & 5.46 & 6.47 & 4.99 & 0.50 & 5.61 & 7.43 & 4.59 & 1.00 \\ AG or BG & 5.48 & 6.57 & 4.95 & 0.57 & 5.69 & 7.87 & 4.57 & 1.19 \\ DE or CF & 5.65 & 6.53 & 5.06 & 0.50 & 5.8 & 7.73 & 4.66 & 1.03 \\ CE or DF & 5.51 & 6.36 & 4.90 & 0.66 & 5.68 & 7.16 & 4.53 & 1.12 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistics of MDB for two control points—over-constrained scenario (**b**). II error probabilities. We highlight that the method discards the use of the observation vector of the Gauss-Markov model. In fact, the only needs are the geometrical network configuration (given by Jacobian matrix) and the uncertainties of the observations (given by instrument precision, measurement techniques and/or field condition). Therefore, it can be applied for any kind of geodetic network. In fact, it can be applied to any mathematical modelling for sensitivity analysis. In other words, it can be employed to apportion the changes in outputs of a system to different configuration in its inputs. The method requires that a priori geodetic network configuration (i.e., geometric configuration and uncertainty of observations) be provided by the user. The user-provided design matrix \(A\) presents rank deficiency due to the lack of definition of a network datum. In other words, there are no minimal constraints to solve a rank deficiency of a priori user-provided design matrix \(A\). In order to have a matrix \(A\) with full rank, the proposed algorithm select a point as control. Next, the maximum external reliability is computed and stored. The procedure is repeated until all network points were selected as a control point. Finally, the algorithm selects the network which has suffered the least influence from a possible undetected outlier. In other words, the algorithm selects that network configuration with the smallest value for maximum external reliability. As shown in the results, the proposed procedure also works for designing geodetic networks with more than one control point. The proposed method was applied to a closed levelling network. The MDB was computed based on a power of the test of data snooping of 0.80 (80%) and the significance level of 0.001 (0.1%). To apply the concepts in practice, two scenarios were presented for a simulated levelling geodetic network: a minimally constrained network one and a over-constrained network. The observations were assumed normally distributed and uncorrelated, which usually happens in the practice of levelling network adjustment. In each scenario two variants were also considered: one in which the variances of the measurements were assumed equal and another in which the variances were different.