|Affiliations:||Mathematical Institute of the Serbian Academy of Sciences and Arts||Title:||Machine learning models for ecological footprint prediction based on energy parameters||Journal:||Neural Computing and Applications||Issue Date:||17-Nov-2020||Rank:||M21||ISSN:||0941-0643||DOI:||10.1007/s00521-020-05476-4||Abstract:||
The ecological footprint is an excellent tool to better understand the consequences of the human behavior on the environment. The growing need for natural resources emphasizes the necessity of their accurate observation, calculation, and prediction. This paper develops and compares four hybrid machine learning models for predicting the total ecological footprint of consumption based on a set of hyper-parameters predefined by the Bayesian optimization algorithm. In particular, K-nearest neighbor regression (KNNReg), random forest regression (RFR) with 93 trees, and two artificial neural networks (ANNs) with two hidden layers were developed and later compared in terms of their performance. As energy inputs, the primary energy consumption from (1) natural gas sources, (2) coal sources, (3) oil sources, (4) wind sources, (5) solar photovoltaic sources, (6) hydropower sources, (7) nuclear sources, and (8) other renewable sources was used. Additionally, population number has also been used as an input. The models were developed using a set of data that include 1804 instances. The ANNs were modeled using two different activation functions in the hidden layers: ReLU and SPOCU. The performance was evaluated using the mean absolute percentage error (MAPE), mean absolute scaled error (MASE), normalized root-mean-squared error (NRMSE), and symmetric mean absolute percentage error (SMAPE). The results show that KNNReg performs the best with MASE of 0.029, followed by the RFR (0.032), ReLU ANN (0.064), and SPOCU ANN (0.089). Moreover, SMOGN was utilized to produce a synthetic test set which was used to additionally test the best performed model. The performance on the SMOGN set demonstrates good performance (MASE=0.022). Lastly, the best performed model was implemented into a GUI that calculates the ecological footprint based on user inputs.
|Keywords:||Ecological footprint | Energy | Machine learning | Modeling | Prediction||Publisher:||Springer Link||Project:||Developing technological processes for nonstandard copper concentrates processing with the aim to decrease pollutants emission|
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checked on Apr 8, 2022
checked on Apr 8, 2022
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