## 4 Discussion
### Added Value and Ranking of Machine Learning Approaches  
Our study indicated that with the mentioned selection of features, most modeling approaches, even the simple multilinear regression, in general agree acceptably well with the aggregated emission value. Although individual realizations of the scenarios (i.e., selections of training data) led to over- and underestimation, in most cases the relative deviation from the actual mean emission value was below 10%. This is considerably better than the \(\pm\)20% range reported for ammonia emission modeling in preceding studies [21; 53]. Only with the simple neural network geometry (fixAnn), the Gaussian Process (GausProc) and some scenarios with the standard multilinear regression (LinReg)a substantial variability of the estimated emission values among the realizations of the scenarios was reported in the current study. This means that it was unlikely to select a suboptimal training period. This is in contrast to the findings in a preceding study on ammonia emission with a similar modeling setup [21]. The main differences between the preceding and the current study were (i) the slightly different choice of features due to the different dependency of the emission dynamics of the two gases on temperature (Section 4.1) and (ii) the data normalization approach. In contrast to the preceding study, here we applied a normalization based on median and quantile range. The latter can reduce the influence of outliers of individual features during the model training. Thus, it can make model projection more robust, since many classifiers use Euclidean distance to evaluated the suitability of selected parameter values. If one feature has a broader range of values, the final value of the distance will be influenced more strongly by this particular feature. Tests of different normalization approaches (results not shown), with the methane emission related dataset used in this study, indicated that the effect of data normalization is particularly relevant for the neural network approaches ANN and fixAnn as well as for GausProc. However, moderate effects were observed also for support vector machines (SVM), and linear regression with and without regularization (Ridge and LinReg), while the ensemble methods (GradBoost and RandFor) were almost insensitive to data normalization. This represents a clear added value of the usage of ensemble methods for the empirical modeling of methane emissions, which can be expected also for the modeling of other gaseous emissions.  
In addition, as found also in the before mentioned study for ammonia, ensemble methods provided one of the most robust projections of the methane emissions in our study (see Section 3.2). We observed that GradBoost and RandFor with reduced measurement setups (scenarios 1-12) achieved similar accuracy as LinReg with extended measurement setups (scenarios 13-27) with six measurement periods, when all input features were used for modeling. In addition, with six measurement periods the ensemble methods were even more reliable in terms of reduced variability of the model projection among the 30 realizations. If only the strongest linearly correlated input features were taken into account in the modeling, LinReg also achieved good results even with reduced measurement setups. In consequence, particularly when only three or four measurement periods are available, a very careful selection of input features or the use of ensemble methods for projecting methane emission values is advisable.