An Intelligent IoT-Cloud-Based Air Pollution Forecasting Model Using Univariate Time-Series Analysis.

Abstract

Air pollution is a significant environmental issue affecting public health and ecosystems worldwide, resulting from various sources such as industrial activities, vehicle emissions, and fossil fuel burning. Air pollution contributes to climate change and can cause several health problems, such as respiratory illnesses, cardiovascular disease, and cancer. A potential solution to this problem has been proposed by using different artificial intelligence (AI) and time-series models. These models are implemented in the cloud environment to forecast the Air Quality Index (AQI)utilizing Internet of things (IoT) devices. The recent influx of IoT-enabled time-series air pollution data poses challenges for traditional models. Various approaches have been explored to forecast AQI in the cloud environment using IoT devices. The primary objective of this study is to assess the efficacy of an IoT-Cloud-based model for forecasting the AQI under different meteorological conditions. To achieve this, we proposed a novel BO-HyTS approach that combines seasonal autoregressive integrated moving average (SARIMA) and long short-term memory (LSTM) and fine-tuned it byusing Bayesian optimization to predict air pollution levels. The proposed BO-HyTS model can capture both linear and nonlinear characteristics of the time-series data, thus augmenting the accuracy of the forecasting process. Additionally, several AQI forecasting models, including classical time-series, machine learning, and deep learning, are employed to forecast air quality from time-series data. Five statistical evaluation metrics are incorporated to evaluate the effectiveness ofmodels. While comparing the various algorithms among themselves becomes difficult, anon-parametric statistical significance test (Friedman test) is applied to assess the performance of the different machine learning, time-series, and deep learning models. The findings reveal that the proposed BO-HyTS model produced significantly better results than their competitor's, providing the most accurate and efficient forecasting model, with an MSE of 632.200, RMSE of 25.14, Med AE of 19.11, Max Error of 51.52, and MAE of 20.49. The results of this study provide insights into the future patterns of AQI in various Indian states and set a standard for these states as governments develop their healthcare policies accordingly. The proposed BO-HyTS model has the potential to inform policy decisions and enable governments and organizations to protect better and manage the environment beforehand.