Multi-Pollutant based Hybrid Framework for Green Smart Cities using Machine Learning Techniques.

Abstract

Achieving a sustainable environment is one of the most emerging issues discussed in the smart cities concept. Due to the rapid population growth in the world, the concentration of greenhouse gases is increasing day by day. A lot of research studies have focused on different techniques and technologies to reduce environmental pollution. To achieve a sustainable environment, it is important to consider the multi-pollutant factors involve in polluting the environment. In MS thesis, we proposed a multi-pollutants-based intelligent hybrid framework using machine learning that considers the multiple sources of pollution in the city environments. The research emphasizes a comprehensive comparisons of machine learning algorithm for predicting the air quality index in smart cities. In this research. The framework considers the concentration of the pollutants in the environment and will make intelligent predictions on their combined effect on the environment as well as the individual groups based on the similar characteristics of gases concerning the guidelines of WHO. The effect of the concentration of multi-pollutants on air quality and water quality will be analyzed. Machine Learning Techniques including Linear Regression, Support Vector Regression, Random Forest Regression, Decision Tree Regression, and Long Short Term Memory (LSTM) will be applied to predict air quality index and for the classification of pollutants. The evaluation measure used in this research is Accuracy, Precision, F1 score and Recall to find the accuracy of these models.