## 4 Discussion
This criterion is however location dependent and cannot be generalized. In contrast to [PERSON] et al., our method automatically extracts features and characteristics that could be hidden within the natural variability, potentially increasing the detection probability. Due to the costly survey in case of confirmation of leakages, it will be of importance with information about the model's uncertainty, our approach provides that. The most important strength of our approach is that it is principled and consistently uses a probabilistic Bayesian framework for effective decision-making in the context of environmental monitoring. A potential weakness of our case study is that we did not systematically optimize hyper-parameters, which could potentially improve the results. Examples are increasing or decrease the number of CNN layers, changing activation functions, loss functions, optimization procedures, and size of filters, strides and kernel size of the convolutional layers. With respect to loss function and activation function, we have chosen the standard techniques used in the machine-learning community today and have not addressed these norms. With respect to size of filters, strides and kernel size of the convolutional layers, we have only performed a small trial and error search of the possible hyper-parameters. Looking into the model's weights shows that quite a large part of the network is active, suggesting that the model structure and size is relatively well-balanced. We have not found it feasible to use a lot of time to tune the model further, as we have achieved good results with the current hyper-parameter configuration. Another more serious limitation is the fact that the BCNN is optimized with data based on a limited number of simulations for a limited time period. This biases the BCNN model towards the simulated conditions. The model can still be used in a general scenario, but its predictive power will decrease. That is why more simulations with different forcing, leak locations and fluxes are necessary to generate a predictive model that is more robust. To test how well our BCNN model generalizes, we have carried out some sensitivity analyses in Section 3.6. The general observation was that adding noise to the test data increased uncertainty and decreased predictive accuracy. With low level of noise the prediction deteriorates, mainly because no-leak time series is miss-classified as leaks, false positives. However, it is still able to distinguish the two classes with relatively good success. Corruption of deep-learning models is a well know problem, even with small alterations in the input data. This problem might have been solved by training the model on noisy data. A second sensitivity analysis showed that removal of the 300T data set reduced the overall predictive power in all cases; however, the method was still able with high confident and accuracy to predict the 300T test data set. This indicates at least good generalization properties to different leakage fluxes, and potentially to different leakage locations. In this study, univariate time series was used; however, if more information were available in terms of different sensors, measuring different geochemical markers, this framework could be extended to a multi-variate TSC setting. Another extension could be to increase the number of classes and treat them as a multi-label classification tasks, with the purpose to classify time series as be either of the following classes; OT, 30T, 300T or 3000T. Another question we would like to study is how well our model generalizes to different locations. One of the key concepts of CNNs is the ability to extract important features from the data. Here the concept capability to capture key characteristics of time series that contain a leak signal or not. In this sense, the model should generalize well to other locations. Testing this hypothesis would be an important step towards wide-spread use of our method.