Machine learning application to industrial analysis of the sugar provision in Matanzas, Cuba
The analysis of ecosystem services can provide important insights into how goods are processed and obtained from the sugar agro-industrial system. For this work, 346 data were collected on the industrial processing of sugarcane in three harvest, in the agroindustry of the Calimete municipality, Matanzas Province (Cuba), with the objective to use the machine learning algorithm, to predict both, biophysical and economic data. Seven predictors were analyzed and by best subset selection, it was identified both the potential yield in sugarcane and the total industrial losses combination to predict the sugar provision service, by multiple linear regression. In addition, it was adjusted a second model to predict the economic effect of the industrial losses. In both models were able to explain over 70 % of the variability observed, in the dependent variables, with a significant F test (p-value: <0.05), also the diagnostic and validation conditions were met.
| Main Authors: | , , |
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| Format: | Digital revista |
| Language: | spa |
| Published: |
Universidad de Ciencias Aplicadas y Ambientales U.D.C.A
2022
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| Online Access: | https://revistas.udca.edu.co/index.php/ruadc/article/view/2334 |
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