Detecção de processos erosivos com aprendizado de máquina e a aplicação da Equação Universal de Perda de Solo (USLE)

Abstract

The research in question investigates the detection of erosion processes through machine learning and the application of the Universal Soil Loss Equation (USLE). The primary issue addressed by the study is the identification and prevention of soil erosion, a significant environmental problem. Key objectives include implementing techniques to early detect erosion processes near power transmission lines, using the USLE as an additional validation factor. The motivation behind the study is to mitigate damage to power distribution networks caused by erosion and to seek effective methods for monitoring and preventing erosion. The methodology involves using machine learning algorithms, specifically Convolutional Neural Networks (CNNs), to analyze satellite images and identify areas prone to erosion. Additionally, the USLE is applied as a tool for calculating soil loss, aiding in the assessment of the likelihood of erosion in specific locations. The main contributions of the study include integrating machine learning technologies in the detection of erosion processes and validating these techniques with the USLE, demonstrating its effectiveness in this context. The results indicate the feasibility and accuracy of using machine learning for detecting erosion processes, as well as the importance of the USLE as an evaluation method. The study’s conclusions highlight the relevance of the practical application of these techniques for the preservation of power transmission lines and more efficient monitoring of their surroundings. Future research directions include expanding the study to other regions and improving the detection methodology.