Multi-layer long short-term memory (LSTM) prediction model on air pollution for Konya province

Abstract

One of the main problems of the developing and changing world is air pollution. In addition to human causes such as population growth, increase in the number of vehicles producing exhaust emissions in line with the population, development of industry, natural causes such as forest fires, volcano eruptions and dust storms also play a role in increasing air pollution. Air pollution has become a bigger problem that reduces the quality of life of living beings and causes various lung and heart diseases due to reasons such as the growing proximity of settlements to industrial zones due to population growth, the increase in the number of individual vehicles, and zoning works carried out by ignoring air quality. Both international organizations and local authorities take various measures to control and prevent air pollution. In Turkey, necessary legal arrangements have been made within the scope of these measures and air quality monitoring stations have been established. The task of these stations is to measure pollutants such as PM10, CO, SO2 together with meteorological data such as air temperature, humidity, wind speed and direction. In this study, a prediction model for the future concentrations of PM10, CO and SO2 pollutants using the measurement data from three different air quality monitoring stations in Konya between January 2020 and January 2021 was realized with a multi-layer Long Short Term Memory (LSTM) artificial neural network. The Root Mean Square Deviation (RMSE) and Mean Absolute Percentage Error (MAPE) methods was used to calculate the performance of the study. As a result of the study, it is observed that the multi-layer LSTM architecture is more successful than the single-layer architecture.