Temporal prediction of carbon monoxide using the Elman Recurrent Neural Network

Abstract

Peatland fires in Indonesia are considered as regional disasters which occur periodically. These negative impacts, especially on our health, continue to threaten the society across the region. The objective of this study was to create a temporal model for predicting the pollutant concentration from peatland fires using the Elman Recurrent Neural Network (ERNN) and training by gathering data from fires which have been occurred recently in Sumatera, Indonesia. The data describing the haze from the peatland fires were generated using the HYSPLIT model with the input of hotspot sequences and meteorological data from NOAA. The stages of the model development consisted of data pre-processing, pollutant concentrations generating using HYSPLIT, pollutant concentration analysis, network architecture formation, weight determination, model training, and the prediction of the model evaluation. Experimental results indicated that the calculation of the ISPU (standard air pollution index) using the GDAS data of 20.5 g / m 3 obtained an ISPU value of 221. This value indicated that the air in the South Sumatera Province was very unhealthy. Similar to the calculation of ISPU using the WRF-Chem data of 26 g / m 3 obtained an ISPU value of 253. This value indicated that air in the South Sumatera Province was very unhealthy.