1
Department of CSE Nalla Malla Reddy Engineering College, Hyderabad
2
Department of CSE , CVR College of Engineering, Hyderabad
Abstract
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.
Sasikala, D., & Sharma, K. V. (2021). Forecasting Fuel Depletion Of Automobiles Through Machine Learning Algorithms. Int. J. of Aquatic Science, 12(3), 1012-1023.
MLA
D. Sasikala; k. Venkatesh Sharma. "Forecasting Fuel Depletion Of Automobiles Through Machine Learning Algorithms". Int. J. of Aquatic Science, 12, 3, 2021, 1012-1023.
HARVARD
Sasikala, D., Sharma, K. V. (2021). 'Forecasting Fuel Depletion Of Automobiles Through Machine Learning Algorithms', Int. J. of Aquatic Science, 12(3), pp. 1012-1023.
VANCOUVER
Sasikala, D., Sharma, K. V. Forecasting Fuel Depletion Of Automobiles Through Machine Learning Algorithms. Int. J. of Aquatic Science, 2021; 12(3): 1012-1023.
)