DeFries, "Extreme Air Pollution in Global Megacities," Current Climate Change Reports, vol. 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. 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. 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. In this study, several analyses are performed. RNN-LSTM is built for sequential data processing such as time-series data. One algorithm that can be used is Recurrent Neural Network - Long Short Term Memory (RNN-LSTM).
With the large amount and variance of data generated from monitoring air quality in Surabaya city, a qualified algorithm is needed to process it. These data are very useful to build a prediction model for the forecast of air pollution in the future. 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. One way to facilitate the prevention of air pollution is to make air pollutionforecasting by utilizing past data.
So it is needed to perform special handling to maintain air quality. If the condition of air is polluted, then the lives of humans and other living things will be disrupted. Air is one of the primary needs of living things.
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