**3.4 Visualization results of the actual collected dataset by applying the selected best ANFIS models**

To confirm the robustness of the selected best models, 2521 collected actual GPS-IMU datasets obtained from the conducted testing from Lumban, Laguna to DLSU, without the need of splitting the data, were used to test and visualize the ANFIS model performances. These actual datasets consist of the uncorrected GPS latitude and longitude coordinates. On the other hand, the corrected GPS latitude and longitude were predicted using the selected highest-performing ANFIS models (ANFIS model 1B for latitude and model 2B for longitude). The visualized map of the plotted actual GPS latitude and longitude dataset and the predicted corrected GPS latitude and longitude through ANFIS are presented in **Figure 13**. It shows that the uncorrected set of GPS latitude and longitude was too outlying and very distant from the predicted corrected output values generated by the ANFIS models. This signifies that the selected ANFIS models have the best performances to predict the correct GPS latitude and longitude.

The response of the predicted corrected GPS coordinates using the selected ANFIS models concerning the reference route and collected actual GPS coordinates are shown (**Figure 14**). The result of the plotted corrected GPS latitude and longitude coordinates (in green dotted line) is almost near the reference GPS coordinates (in blue line) compared to the collected actual GPS latitude and longitude (in red pin markers). According to the results, the proposed ANFIS models, that is, the fuzzy system combined with neural network capabilities, achieved better error reduction without the need to identify the system's noise type, as it was trained on the

#### **Figure 13.**

*Visualization maps of the uncorrected GPS coordinates (red pin marker) versus the predicted corrected GPS coordinates (green dots).*

#### **Figure 14.**

*Resulting visualization maps for comparison of the (a) reference GPS coordinates, (b) uncorrected GPS coordinates, and (c) corrected GPS coordinates using the ANFIS models.*

data region. This makes it a more convenient and cost-effective option than other state-of-the-art approaches. In the given noisy collected actual dataset, the selected ANFIS models have been proven to address the challenging and nonlinear problems while minimizing complexity in computation [27]. Because ANFIS models may be implemented in real-time, they are well suited for applications that demand rapid and precise data processing, particularly suitable for the localization and tracking of vehicle position [27]. Integrating the ANFIS models into the capacitive resistivity underground imaging towed antenna system can offer great significance in mapping the precise location of the surveyed underground utility objects.

However, verifying further the ANFIS models' performance requires the recollection of new GPS latitude and longitude datasets and testing this new data to the simulated ANFIS models. This is considered as the lacking approach of this study that can be done for future research. Additionally, to maximize the ANFIS model performance, it suggests retraining and adjusting the hyperparameters when tested in newly collected actual data. Also, the study's results can be further evaluated by comparing them with other methods of sensor fusion such as the use of other machine learning models which will be the next direction of the paper.
