A comparative analysis of LSTM and ARIMA for enhanced real-time air pollutant levels forecasting using sensor fusion with ground station data

Abstract

One of the most basic needs of any human being to survive is air. Unfortunately, this basic need is being polluted by many natural factors like volcanic eruptions, forest fires, and man-induced factors like transportation emission. Unpolluted air is now an ideal environment that can never be achieved. So, the pollution levels should be monitored continuously. However, monitoring the levels of pollution will not fix the environment. Forecasting these pollution levels can make the society more aware of the environment and help prepare safety measures. This research paper aims to forecast the air pollutant levels by comparing futuristic machine learning models, which are Long Short-Term Memory (LSTM) and Auto-Regressive Integrated Moving Average (ARIMA) performed on the ground station data from Central Pollution Control Board (CPCB), the data collected from a low-cost IoT hardware setup, and the fused data. The LSTM and ARIMA models have been used to forecast the air pollutant levels in the future. Further, the main novelty of this research is to show that the concept of sensor fusion increases the accuracy of the dataset. The outputs obtained after implementing LSTM and ARIMA models show more accurate results when compared with ground station data and the IoT data from sensors.

Keywords

• air pollutant levels
• machine learning
• forecasting
• arima
• lstm
• sensor fusion
• iot