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 dangerous pollutants thus damaging the ecosystem.
To keep pollutants in check 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 input features as well as the target variable representing the emission band of the vehicle.
In addition to the big data available as mentioned above, major advancements in the machine learning algorithms are done today. The advent of algorithms such as Artificial Neural Networks (ANN) has made it possible to develop models with very high accuracy.
In this paper, the authors therefore propose the application of Extreme Learning Machines (a type of feedforward neural networks) to solve the pressing challenge of classifying 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 is scalable such as to the task confronted with in this paper. Results from the metric, namely classification accuracy, 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 already in place today for a greener earth.