Extreme Learning Machines (ELM) for Fast Instantaneous Prediction of Emission Level of an Automobile

Addressing climate issues is a key aspect of good global governance today. A key aspect of managing the threats caused to the environment around is to ensure a sustainable transportation system so that humans exist in peace with nature. According to sources, in 2020 alone, cars accounted for approximately 23% of global CO2 emissions. In addition, they also emit nitrogen oxides and other pollutants thus damaging the ecosystem. In line with this objective, there are emission level testing strategies in place in each country. However, we can do better for a sustainable future. On one hand, the huge volume of vehicles around the world makes it an excellent choice and source for a vast emission level dataset comprising of features such as vehicle type, fuel type, driving conditions, road type etc.. as well as the target variable representing the emission band of the vehicle. In addition to the big data available as mentioned above, there is a marked progress in the machine learning algorithms today. The advent of algorithms such as neural networks has made it possible to develop models with very high accuracy. In this paper, we therefore propose the application of Extreme Learning Machines (a type of feedforward neural networks) to solve the pressing challenge of predicting the emission band of a vehicle which can be used by agencies to ensure that healthy vehicles operate on road at large. Extreme Learning Machines (ELM), by definition, is an excellent choice for emission band prediction task as it offers a significant reduction in training time and thereby scales well with large datasets such as the task we are confronted with in this paper. Results from metrics such as classification accuracy, precision etc.. are discussed at length. The highly accurate trained model, thus developed, can be used by agencies to then predict the emission band for any given vehicle scenario thus complementing the strategies we already have in place today for a greener earth.