Modelado basado en redes neuronales artificiales: Memoria de largo-corto plazo para la contaminación en Lima Metropolitana

Abstract

Particulate matter (PM) is a mixture of fine dust and tiny droplets of liquid suspended in the air. PM10 are pollutant particles with a diameter of less than 10 micrometers. These particles are harmful to the respiratory system. The air quality in the region and capital Lima in the Republic of Peru has been investigated in recent years. In this context, statistical analyses of PM10 data with forecast models can contribute to planning actions that can improve air quality. The objective of this work is to perform a statistical analysis of the availablePM10 data and evaluate the quality of time series classical models and neural networks for short-term forecasting. The Box-Jenkins models showed the best performance for short-term forecasting compared to the neural network models considered.