## 5 Conclusions  
Event tracking sensitivity analysis (ETSA) has been applied to facilitate drilling disaster prediction in which the effect of system feature inputs on its state is continuously scored, as input events could cause this state to change. ETSA can reduce the computational burden of dealing with a large number of datasets that may not all necessarily contribute to building an effective processing model. This was demonstrated in this paper by feeding the introduced feature datasets into the ETSA algorithm. It was then observed that the resulting Sensitivity Indices correlated well with the results of other classification methods used in this paper, including Random Forest Classifiers (RFC) and Neural Networks (NN). Without SA, only expert knowledge decides which of the 100 features is used as an input to the two state-of-the-art event classifiers, RFC and NN. SA was adopted in order to assess whether or not it can provide an alternative method to feature selection that depends on expert knowledge alone or provides a cross-check for it. In the event that expert knowledge is not available, it was shown that the SA outputs could feasibly be used in place of the expert's input. In addition, the SA (EventTracker) does this in real time. For example, the time performance of the ETSA method was shown to support a time-critical  
Figure 14: Feature Selection Results—All Wells, 1 st SFS Cycle. application of data dimensionality reduction for a drilling disaster prediction system. No other SA approach can currently do this in real time. Oil firms would benefit from this research by replacing expensive and time-consuming expert knowledge that is utilized at the stage of feature selection by the introduced EventTracker Sensitivity Analysis method. In this work, the methods were applied to the data volumes on the scale of 1000 features. Further research could benefit from covering this limitation by applying the same methodology to much a higher scale of data sources and features that apply to the other sectors of industry, such as manufacturing that usually deals with more than 10,000 data sources and features. Such research is well underway by the main author of this article using state-of-the-art computation platforms. In addition, future work on the development of the ETSA method is expected to be able to generate higher values for the correct classification rate (CCR) so that, as a result, incorporation of expert knowledge is less critical to preventing environment crisis events. There are two ways to achieve this improvement. First, the search parameters of the implemented algorithm for the ETSA method that are currently fixed values will be evaluated adaptively and dynamically. This will allow the events in the data series to be detected more effectively without the use of expert knowledge. Second, the current method measures the sensitivity of the system output with respect to individual inputs. Accounting for this, the impact of the combination of the inputs on the system's output will be investigated. Although this will result in a more exhaustive algorithm in terms of time and complexity, both Neural Network-based and Random Forest Classifier-based classification methods can benefit from the generated combined sensitivity indices as the number of dimensions to be processed is reduced. This is especially true for the Neural Network-based method, but also for the Random Forest Classifier-based method where a lower number of features might reduce the probability of incorrect decisions in early levels of the trees. A further interesting aspect for future work would be the development of an ETSA-focused classification algorithm and working to determine the optimal benefit that the features selected by ETSA provide for different classification methods. Conceptualization, S.T. ; Methodology, S.P., M.W. and H.Z. ; Software, S.T. and M.W. ; Validation, S.T. and H.Z. ; Investigation, S.T. and S.P. ; Resources, M.W. and H.Z. ; Data curation, R.F. ; Writing--original draft, R.F., M.W. and H.Z.