The presence of green areas in urbanized cities is crucial to reduce the negative impacts of urbanization. However, these areas can influence the signal quality of IoT devices that use wireless
communication, such as LoRa technology. Vegetation attenuates electromagnetic waves, interfering with data transmission between IoT devices, resulting in the need for signal propagation
modeling that considers the effect of vegetation on its propagation. In this context, this research
was conducted at the Federal University of Pará, using measurements in a wooded environment
composed of the Pau-Mulato species, typical of the Amazon. Two propagation models based on
machine learning, GRNN and MLPNN, were developed to consider the effect of Amazonian
trees on propagation, analyzing different factors such as the height of the transmitter relative to
the trunk, the beginning of the foliage, and the middle of the tree canopy, as well as the LoRa
spreading factor (SF) 12 and the copolarization of the transmitter and receiver antennas. The
best models were the machine learning ones, GRNN and MLPNN, which demonstrated greater
accuracy, achieving root mean square error (RMSE) values of 3.86 dB and 3.8614 dB, and
standard deviation (SD) of 3.8558 dB and 3.8564 dB, respectively. On the other hand, compared
to classical models in the literature, the best-performing model was the Floating Intercept (FI)
model, with RMSE and SD errors around 7.74 dB and 7.77 dB, respectively, while the FITU-R
model had the highest RMSE and SD errors, around 26.40 dB and 9.65 dB, respectively, for
all heights and polarizations. Furthermore, the importance of this study lies in its potential to
boost wireless communications in wooded environments, as it was observed that even at short
distances at heights of 12 m and 18 m, the SNR (Signal-to-Noise Ratio) had lower values due
to the influence of the foliage, but it was still possible to send and receive data. Finally, it was
shown that vertical polarization achieved the best results for the Amazon forest environment.
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