Technogenic Reservoirs Resources of Mine Methane When Implementing the Circular Waste Management Concept

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Abstract

From a commercial viewpoint, mine methane is the most promising object in the field of reducing emissions of climate-active gases due to circular waste management. Therefore, the task of this research is to estimate the technogenic reservoirs resources of mine methane when implementing the circular waste management concept. The novelty of the authors’ approach lies in reconstructing the response space for the dynamics of methane release from the front and cross projections: CH4 = ƒ(S; t) and CH4 = ƒ(S; L), respectively. The research established a polynomial dependence of nonlinear changes in methane concentrations in the mixture extracted by type 4 wells when a massif is undermined as a result of mining in a full-retreat panel. And the distance from the face to the start of mining the panel is reduced by 220 m. For this reason, the emission of mine methane, in case of degasification network disruption in 15 days, can amount to more than 660 thousand m3 only for wells of type no. 4.

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The coefficient of determination R-squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation

Davide Chicco, Matthijs Warrens, Giuseppe Jurman (2021)

Regression analysis makes up a large part of supervised machine learning, and consists of the prediction of a continuous independent target from a set of other predictor variables. The difference between binary classification and regression is in the target range: in binary classification, the target can have only two values (usually encoded as 0 and 1), while in regression the target can have multiple values. Even if regression analysis has been employed in a huge number of machine learning studies, no consensus has been reached on a single, unified, standard metric to assess the results of the regression itself. Many studies employ the mean square error (MSE) and its rooted variant (RMSE), or the mean absolute error (MAE) and its percentage variant (MAPE). Although useful, these rates share a common drawback: since their values can range between zero and +infinity, a single value of them does not say much about the performance of the regression with respect to the distribution of the ground truth elements. In this study, we focus on two rates that actually generate a high score only if the majority of the elements of a ground truth group has been correctly predicted: the coefficient of determination (also known as R -squared or R 2 ) and the symmetric mean absolute percentage error (SMAPE). After showing their mathematical properties, we report a comparison between R 2 and SMAPE in several use cases and in two real medical scenarios. Our results demonstrate that the coefficient of determination ( R -squared) is more informative and truthful than SMAPE, and does not have the interpretability limitations of MSE, RMSE, MAE and MAPE. We therefore suggest the usage of R -squared as standard metric to evaluate regression analyses in any scientific domain.