Intelligent information system for resource planning in grain crops delivery projects on the basis of machine learning

Tryhuba, Anatoliy; Kotenko, Viktoriia

doi:10.1109/csit61576.2023.10324162

Cited by 1 publication

(1 citation statement)

References 25 publications

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“…However, the use of modern approaches for forecasting specific fuel consumption involves the use of large databases describing transportation processes at specific enterprises. Among such approaches, machine learning methods are particularly relevant now [7][8][9]. Forecasting specific fuel consumption using machine learning methods allows for establishing regularities and identifying relationships between data.…”

Section: Analysis Of the Recent Research And Problem Statementmentioning

confidence: 99%

Data Justification for Forecasting Fuel Consumption by Vehicles During the Grain Crops Delivery

КОТЕНКО

2024

Automash

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The paper provides a justification of data for forecasting fuel consumption by vehicles during the transportation of grain crops from agricultural enterprises to the elevator. In order to obtain knowledge about the influence of the peculiarities of providing transport services and the conditions of using vehicles on the specific fuel consumption, regularities have been established and correlations between individual sets of data have been identified. To analyze data regarding the use of vehicles during the transportation of grain crops and to obtain additional information, a database containing 14140 instances of data on orders for the delivery of grain crops from agricultural enterprises to the elevator was used. Factor analysis was performed and trends in the change of indicators characterizing these orders were identified. The analysis showed that the specific fuel consumption during the transportation processes of delivering grain crops from agricultural enterprises to the elevator is determined by a multitude of specific factors. Additionally, each order for the delivery of grain crops from agricultural enterprises to the elevator has its own specifics. The results revealed the following relationships between specific fuel consumption and identified factors: the specific fuel consumption indicator is influenced by the location and method of vehicle loading, the vehicle model, the type of cargo, distance, cargo turnover and cargo volume. The identified dependencies allow for the preparation of a dataset and the correct interpretation of the results of machine learning models' work in order to increase the efficiency of their forecasting. Key words: specific fuel consumption, transportation process, transportation of grain crops, factor analysis, machine learning

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Section: Analysis Of the Recent Research And Problem Statementmentioning

confidence: 99%

Data Justification for Forecasting Fuel Consumption by Vehicles During the Grain Crops Delivery

КОТЕНКО

2024

Automash

View full text Add to dashboard Cite

show abstract

Intelligent information system for resource planning in grain crops delivery projects on the basis of machine learning

Cited by 1 publication

References 25 publications

Data Justification for Forecasting Fuel Consumption by Vehicles During the Grain Crops Delivery

Data Justification for Forecasting Fuel Consumption by Vehicles During the Grain Crops Delivery

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