## 2 Methodology
### Coding and Software Libraries
#### 2.2.2 Feature Selection  
Feature selection, a crucial step in data pre-processing, has demonstrated its effectiveness and efficiency in readying high-dimensional data for various data analysis and machine learning tasks. The primary goals of feature selection are to simplify models, enhance predictive accuracy, enhance comprehensibility, and make inferences about the data [27]. Generally, various methods for feature selection can be categorized into three categories: filter, wrapper, and embedded methods [28]. The filter approach leverages the inherent features of the training data without relying on the specific predictive algorithm being used [28; 29]. On the other hand, the wrapper method evaluates the correlation between feature relevance and optimal feature subset selection by searching for the best subset of features that aligns with the chosen predictive algorithm [28; 29]. Lastly, the embedded approach integrates feature selection within the training process by utilizing a learning algorithm specifically designed for this purpose [28]. The Boruta feature selection method was selected for this study. Boruta is a wrapper method of feature selection, meaning it uses a model to evaluate the importance of each feature and select the most relevant features. The Boruta was selected because it implements an all-relevant approach, considering all features that impact the outcome variable. It is \begin{table}
\begin{tabular}{c c c} \hline \hline
**Variable Index** & **Variable** & **Description** \\ \hline
1 & Accident psig & Estimated pressure at the point and time of the incident \\
2 & Incident year & Year of incident \\
3 & Location datetime & Earliest local time and date an incident reporting criterion was met \\
4 & Release type & Type of release involved \\
5 & Longitude & Location longitude \\
6 & Latitude & Location latitude \\
7 & Commodity released type & Type of gas commodity released \\
8 & Unintentional release & Estimated volume of gas released unintentionally \\
9 & Number of people evacuated & Number of members of the general public evacuated \\
10 & Incident identified date time & Local time operator identified failure \\
11 & Incident area type & Area of incident \\
12 & Operator identity & Ops-issued operator identification number \\
13 & Depth cover & Depth of cover \\
14 & Crossing & Type of crossing \\
15 & Pipe facility type & Indicates the type of pipeline system \\
16 & System part involved & Part of the system involved in incident \\
17 & Installation year & The year the item involved in the incident was installed \\
18 & Pipe diameter & Nominal pipe size \\
19 & Pipe material & Material involved in incident \\
20 & Temperature & Temperature recorded at the location, date, and time at which the incident \\  & & incident occurred \\
21 & Humidity & Humidity recorded at the location, date, and time at which the incident \\  & & occurred \\
22 & Wind speed & The wind speed recorded at the location, date, and time at which the incident occurred \\  & & Atmospheric pressure recorded at the location, date, and time at which \\
23 & Pressure & Atmospheric the incident occurred \\
24 & Normal psig & Normal operating pressure at the point and time of the incident \\
25 & The estimated cost of gas & Cost of gas in USD per thousand standard cubic feet (mcf) \\
26 & Cost of operator & The estimated cost of the operator’s property damage and repairs \\
27 & Cost of emergency response & The estimated cost of emergency response \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Sample of variables influencing pipeline damage description. designed to handle datasets that have a large number of variables, including variables that may be correlated or redundant. In contrast, many other variable-selection algorithms follow a minimal optimal strategy, selecting only a small subset of features that produce a minimal error when using a specific classifier [30]. The pseudocode for implementing the Boruta feature selection is given as follows in Algorithm 1.