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Neural Modeling of Greenhouse Gas Emission from Agricultural Sector in European Union Member Countries

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The present paper discusses a novel methodology based on neural network to determine agriculture emission model simulations. Methane and nitrous oxide are the key pollutions among greenhouse gases being a major contribution to climate changes because of their high potential global impact. Using statistical clustering (k-means and Ward’s method), five meaningful clusters of countries with similar level of greenhouse gases emission were identified. Neural modeling using multi-layer perceptron networks was performed for countries placed in particular groups. The parameters that characterize the quality of a network are the predictive errors (mainly validation and test) and they are high (0.97–0.99). The use of sensitivity analysis allowed for identifying the variables that have a significant influence on the greenhouse gases emissions. The sensitivity analysis of the designed artificial neural network models shows a few dominant variables, affecting emissions with varied intensity: cattle and buffaloes, sheep and goat populations, afforestation as well as electricity consumption. The observed values were compared with those predicted by the models. The forecasted course of changes in the variable test is identical with the real data, which proves that the model highly matches to the observed data.

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Correspondence to Alicja Kolasa-Więcek.

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Kolasa-Więcek, A. Neural Modeling of Greenhouse Gas Emission from Agricultural Sector in European Union Member Countries. Water Air Soil Pollut 229, 205 (2018).

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  • Greenhouse gases
  • Agriculture emission
  • Neural modeling
  • Multi-layer perceptron
  • Clustering method
  • UE