## 7 Conclusions  
This work aimed to develop two propagation models using machine learning techniques, MLPNN and GRNN, for densely forested environments at a frequency of 915 MHz, and to evaluate them in relation to measured data and existing models in the literature. To achieve this, an extensive measurement campaign was conducted in the Camilo Vianna forest, located within the Federal University of Para, which features various tree species, predominantly the Pau-Mulato type. In this measurement campaign, data related to geolocation, RSSI, and SNR were collected along a 250 m path. Subsequently, there was a need for data processing, such as calculating the distance using geolocations and calculating propagation loss using RSSI and SNR data. Furthermore, artificial neural networks (MLPNN and GRNN) were trained, in addition to the application of least squares technique to adjust the CI and FI propagation models. The objective was to predict the average behavior of signal propagation and evaluate them in relation to the measured data. Subsequently, they were compared with models adapted for vegetation (ITU-R, Weissberger, COST235, and FITU-R). Thus, the proposed propagation models, MLPNN and GRNN, achieved better accuracy and precision, with RMSE values of 3.8614 and 3.86, and standard deviation values of 3.8564 and 3.8558, respectively, for RSSI. In addition, for the SNR prediction, the RMSE values were 2.3801 and 2.3805, and the standard deviation values were 2.3788 and 2.3798, respectively. The results show the models' ability to learn from data and capture nonlinear relationships. This ability allows these models to achieve greater predictive capability of the average signal behavior compared to classical models in the literature. The satisfactory results obtained by the MLPNN and GRNN ANNs in modeling propagation loss indicate that both are viable options for this regression task. With minimal difference in the RMSE results, the choice between the two can be guided by training speed and resource demand, with the GRNN having an advantage in these aspects. While the CI and FI models performed overall better than vegetation-adapted propagation models, it is worth noting that both were adjusted with measured data from the environment, as shown in Table 5. As for models adjusted for vegetated environments, the COST235 propagation model generally better fitted the measured data than other vegetation models. Another significant contribution of the work was the analysis regarding heights and co-polarizations. The appropriate choice of electromagnetic wave polarization for wireless signal transmissions in vegetated environments is crucial to optimize the efficiency and reliability of communications. This research demonstrated that VV polarization is more suitable for all heights. Understanding these differences allows for the design of more efficient and resilient communication networks, contributing to advancements in monitoring and research applications in densely forested environments. Finally, another contribution was calculating the values of PLE, \(\alpha\), and \(\beta\) for the study environment, as shown in Table 5.  
For future work, the intention is to analyze the impact of spreading factor, and consequently, the bit rate, as the two are directly related. Also, analyze the heights higher than 18 m and lower than 6 m to quantify the impact of dense vegetation on electromagnetic signals. To examine the cross-polarizations of transmitter and receiver, antennas for different heights need to be used in order to understand which polarization type experiences less loss through the forest. Additionally, due the propagation loss imposed by vegetation on transmission across these various scenarios, the intention is to utilize other machine learning techniques (such as ANFIS) and statistical metrics (including MAE, MPE, MAPE, and GRG-MAPE), as well as employ the ITU-R P1546-6 model as it is the new extension of the 1546 model for distances shorter than 1000 km.