**5. Conclusions**

This paper proposed the use of a machine learning algorithm, namely GBDT, in order to estimate methane adsorption isotherm on coals that are based on coal properties (ash, fixed carbon, inherent moisture, and vitrinite contents and vitrinite reflectance), equilibrium moisture content and temperature. Laboratory tests, including proximate analysis, maceral group identification, vitrinite reflectance determination, and adsorption isotherm measurements, were conducted on 165 coal samples retrieved from the Qinshui basin in China in order to develop a database for regression. It has been demonstrated that the GBDT is capable of not only reproducing the adsorption isotherms with reasonable accuracies, but also properly recovering the underlying relation between the input and output variables. As a comparison, the BP-ANN is associated with the over-fitting problem, whereas the SVM has di fficulties in accurately estimating the adsorption isotherms in both the training and testing stages. Such observations confirmed the superiority of the GBDT over other ML tools in solving the specific regression problem in this study. Furthermore, the relative importance scanning and univariate analysis based on the constructed GBDT model showed that the adsorption isotherms are primarily controlled by the fixed carbon and ash contents for the coals that were investigated in this study. Other factors, including vitrinite, inherent and equilibrium moistures, vitrinite reflectance, and temperature, exert minor or even negligible e ffects on the adsorption isotherm.

**Author Contributions:** Conceptualization, Q.F.; methodology, J.Z.; software, X.Z.; validation, N.W.; formal analysis, X.Z.; investigation, J.Z.; data curation, J.Y.; writing—original draft preparation, J.Z.; writing—review and editing, X.Z.; visualization, J.Y.; supervision, Q.F.; project administration, Q.H.; funding acquisition, J.Z. and X.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by China National Natural Science Foundation, gran<sup>t</sup> numbers 51904319 and U1810105, China Postdoctoral Science Foundation, gran<sup>t</sup> number 2018M642727.

**Conflicts of Interest:** The authors declare no conflict of interest.
