SMART ENVIRONMENT INDEX PREDICTION OF SMART CITY USING POLYNOMIAL REGRESSION

Abstract

Smart Environment refers to environment where pollution is detected, predicted, classified and solved using smart tools and technology such as using Internet of Things (IoT) sensors, cloud service, and machine learning algorithms. Episodic environment index prediction allows governments and local city agency to detect pollution in environment at a premature stage to initiate proper steps. Advancements in machine learning, sensor, and camera technology have endorsed us to do that, which can benefit millions of cities and its citizens indeed. This paper describes a new method for smart environment index prediction based on index of five others smart city components using polynomial regression. This research mainly works with the Smart Cities Index dataset. We attempted numerous steps to select feature and to filter its contents to make them more apposite for selected regression algorithm. Our approach unites several feature selection techniques, outlier detection technique, polynomial degree selection technique, and random state selection technique, which outcomes in a reduction of Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE) to 0.0000000140, 0.000000000000000322, and 0.0000000179 respectively and rise R2 to 1.0 for prediction. This method can be a valuable tool for smart environment index prediction from smartness of various components of a city, thus drastically reducing survey or other related works.