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Communication Dans Un Congrès Année : 2020

Environmental Impact Classification of Perishable Cargo Transport Using Data Mining

Manoel Eulálio Neto
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Nilsa Silva Lima
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Résumé

The study presents a model for classifying the environmental impact caused by the transport of vegetables from the production centers of several Brazilian states to a distribution center in Teresina, Brazil, using data mining. The distances from production regions to the distribution center were calculated. CO2-eq emissions and Global Warming Potential (GWP) were estimated. The GWP indicates the potential for the environmental impact that gas causes in each period (usually 100 years). We applied the data mining approach using the Rapid Miner Studio® software to build up the models. The target was the environmental impact indexed as “low”, “average”, and “high”. Results indicated that considering the on-road modal transport presented in the trees, the “product,” “distance,” and “quantity” classification for high environmental impact depends on the amount of product transported as well as the distance traveled. The found trees classify the impact and can be used as guidance for the decision-maker, as it can be used when planning and purchasing fruit and vegetables for public consumption.
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Licence : CC BY - Paternité

Dates et versions

hal-03635656 , version 1 (20-06-2023)

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Manoel Eulálio Neto, Irenilza De Alencar Nääs, Nilsa Silva Lima. Environmental Impact Classification of Perishable Cargo Transport Using Data Mining. IFIP International Conference on Advances in Production Management Systems (APMS), Aug 2020, Novi Sad, Serbia. pp.624-630, ⟨10.1007/978-3-030-57997-5_72⟩. ⟨hal-03635656⟩
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