PREDICT URBAN AIR POLLUTION IN SURABAYA USING RECURRENT NEURAL NETWORK – LONG SHORT TERM MEMORY

Abstract

Air is one of the primary needs of living things. If the condition of air is polluted, then the lives of humans and other living things will be disrupted. So it is needed to perform special handling to maintain air quality. One way to facilitate the prevention of air pollution is to make air pollution forecasting by utilizing past data. Through the Environmental Office, the Surabaya City Government has monitored air quality in Surabaya every 30 minutes for various air quality parameters including CO, NO, NO2, NOx, PM10, SO2 and meteorological data such as wind direction, wind direction, wind speed, wind speed, global radiation, humidity, and air temperature. These data are very useful to build a prediction model for the forecast of air pollution in the future. With the large amount and variance of data generated from monitoring air quality in Surabaya city, a qualified algorithm is needed to process it. One algorithm that can be used is Recurrent Neural Network - Long Short Term Memory (RNN-LSTM). RNN-LSTM is built for sequential data processing such as time-series data. In this study, several analyses are performed. There are trend analysis, correlation analysis of pollutant values to meteorological data, and predictions of carbon monoxide pollutants using the Recurrent Neural Network - LSTM in the city of Surabaya correlated with meteorological data. The results of this study indicate that the best prediction model using RNN-LSTM with RMSE calculation gets an error of 1,880 with the number of hidden layer 2 and epoch 50 scenarios. The predicted results built can be used as a reference in determining the policy of the city government to deal with air pollution going forward.