Forecasting Fuel Depletion Of Automobiles Through Machine Learning Algorithms

Document Type : Primary Research paper


1 Department of CSE Nalla Malla Reddy Engineering College, Hyderabad

2 Department of CSE , CVR College of Engineering, Hyderabad


Forecasting fuel depletion of automobiles - project targets at predicting the
intake of fuel by automobiles which is vital in improving fuel economy of automobiles and
preventing fraudulent activities. In the globe currently fuel plays a major role in
transportation domain. Distance, capacity, automobile features, and motorist performance
are the internal factors influencing the fuel depletion and pathway conditions, traffic flow,
and climate shows a dynamic part of external factors. The foremost task is to model and
predict the fuel depletion only with the available data with the stimulus of internal and
external factors. However, a few of these factors are measured or available for the fuel
depletion analysis. That is, a case is considered where only a subset of the above-mentioned
factors is available as a multi-variate time series from a long distance, for different vehicles
say, public and private bus, cabs etc.,. The recommended system using Machine Learning
(ML) algorithms - Linear Regression Method, Multi Variate Method, and Random Forest
Method performs a significant part in prediction. Random Forest Method overtakes the
other two ML algorithms in its accomplishment. These predictions also assist in realizing
how they can serve in progress of the ecosystem.


Volume 12, Issue 3 - Serial Number 3
ICMMNT-2021 International Virtual Conference on Materials, Manufacturing and Nanotechnology, 30th June, 2021.
June 2021
Pages 1012-1023