A Digital Twin based Approach for Healthier Smart City Environment

Abstract

Air quality, toxic substances, climate change, pollution, and occupational hazards are the environmental factors that have a great impact on human and environmental health. The presence of pollutants and contaminants in the environment can have detrimental effects on human health, including respiratory issues, cardiovascular ailments, cancer, neurological disorders, and various other illnesses. Certain groups, such as children, older adults, and individuals with pre-existing health conditions, are more vulnerable to the health impacts of environmental hazards. Air Quality Index is used to depict the air quality of an area. This thesis delves into studying the utilization of Digital Twin models as an innovative strategy for creating a sustainable smart city environment by accurately forecasting Air Quality Index (AQI) using time series analysis. The study applies different deep learning models, namely Gated Recurrent Units (GRU), Long Short Term Memory (LSTM), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and deep Artificial neural networks (ANN) for precisely predicting the AQI levels for a healthier environment. The time-series historical data of Delhi city has been gathered from the year 2015 to 2020 and is preprocessed before proceeding to train and validate different deep learning models. The six selected machine learning algorithms have been implemented and it has been observed that CNN offers unparalleled accuracy compared to other evaluated models making it highly effective for precise forecasting. The CNN-1D-2 layer yielded the best results with root mean squared error (RMSE) reaching 3.010343, the mean absolute error (MAE) reaching 1.706329, the mean absolute percentage error (MAPE) reaching 0.013216, and the R2 reaching 0.99931. The digital twin model is developed by incorporating InfluxDB and Grafana. InfluxDB is an open-source platform that has been used to store the historical and real-time data of AQI using python. The Grafana online platform is utilized for data visualization and management and facilitates real-time monitoring. The 3D model of the city is developed in Blender and then the 3D file is exported to Microsoft Azure Digital Twin explorer to develop a digital twin using Digital Twin Definition Language (DTDL). The digital twin model displays the forecasted value of AQI and different pollutants along with their previous trends in the form of graphs. The 3D model of the city is covered with different colors based on ranges or thresholds defined for AQI. The findings contribute to the advancement of smart city development by utilizing digital twin-based approaches for creating healthier and more sustainable urban environments