Métodos de aprendizado de máquina aplicados ao monitoramento ambiental

Abstract

In recent years, the world has been undergoing enormous environmental transformations and where there is a constant occurrence of major technological innovations, the need arises to understand how technology can be beneficial to the environment. Neural Networks have emerged as powerful tools for classifying complex data, offering solutions for identifying patterns and anomalies. This article explores the application of three neural networks – Artificial Neural Networks (ANN), Multilayer Perceptron (MLP) and Radial Basis Function Networks (RBF) – in the context of urban environmental data classification. We present a comparative analysis of their performance using real datasets, highlighting their strengths and limitations in accurately categorizing environmental parameters. Our results demonstrate the effectiveness of these neural network models in contributing to environmental monitoring, providing valuable insights.