## 3 Results
The natural gas pipeline systems are vulnerable to various effects of climate change, including alterations in temperature ranges, heightened thermal stress, increased runoff leading to heavy precipitation and flooding, and more frequent occurrences of hurricanes, landslides, and land subsidence (44). This will enable a more comprehensive understanding of their effects and help to prevent disruptions to the distribution of gas products to consumers. The complex and interconnected nature of natural gas pipelines, coupled with the prolonged economic lifetime of the infrastructure, makes it crucial to address and adapt to the challenges posed by climate change (45). The selected features were used in the subsequent modeling process to develop a predictive model for pipeline damage. Overall, the results from the Boruta feature selection process provided valuable insights into the most important features that affected pipeline damage and improved the accuracy of the predictive model. Table 3 shows a statistical summary of the response and the obtained predictive variables. The K-nearest neighbor (KNN) algorithm was used with the grid search method to classify the pipeline damage dataset. The dataset consisted of 12 relevant features obtained through the Boruta feature selection process. The grid search technique was employed to find the optimal hyperparameters for the KNN model, which included the number of neighbors (\(k\)) and the distance metric for classification. The results of the grid search showed \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Variables** & **Count** & **Mean** & **STD** & **Min** & **25\%** & **50\%** & **75\%** & **Max** \\ \hline Natural Force Type & 81 & 3.04 & 1.95 & 0 & 1 & 3 & 4 & 7 \\ Age & 81 & 39.67 & 25.55 & 1 & 23 & 38 & 52 & 109 \\ Temperature & 81 & 52.95 & 22.92 & 2 & 35 & 57 & 72 & 92 \\ Humidity & 81 & 73.74 & 19 & 9 & 61 & 77 & 90 & 100 \\ Latitude & 81 & 40.74 & 4.55 & 30.34 & 38.96 & 41.12 & 43.32 & 60.57 \\ Longitude & 81 & \(-\)90.25 & 14.83 & \(-\)151.3 & \(-\)95.27 & \(-\)88.94 & \(-\)80.25 & \(-\)71.09 \\ Pipeline Diameter & 81 & 2.25 & 3.433 & 0 & 0 & 0.625 & 4 & 16 \\ Pipeline Material & 81 & 2.64 & 1.49 & 0 & 2 & 3 & 4 & 5 \\ Number of Residences Affected & 81 & 103.17 & 364.11 & 0 & 1 & 1 & 17 & 2584 \\ Number of Commercial Affected & 81 & 3.65 & 1.15 & 0 & 0 & 0 & 0 & 770 \\ Crossing & 81 & 0.06 & 0.24 & 0 & 0 & 0 & 0 & 1 \\ Cost of Operator & 81 & 588,721 & 1,889,704 & 0 & 58,420 & 112,502 & 287,156 & 15,400,000 \\ Injury patient Hospitalization & 81 & 0.17 & 0.378 & 0 & 0 & 0 & 0 & 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Statistical summary of response and predictive variables. that \(k=3\) and the Manhattan distance metric were the optimal hyperparameters for the KNN model. After training the KNN model using the optimal hyperparameters, the model was evaluated using the testing data. The model achieved an accuracy of 74% (Figure 5) on the testing dataset, indicating that it was able to classify pipeline damage instances with acceptable accuracy. However, accuracy alone does not provide a comprehensive picture of the model's performance, especially in the case of imbalanced classes. To further assess the model's performance, precision (Equation (3)) and recall (Equation (4)) values were computed for each class. In a classification problem, a class refers to a category or label that an observation or data point belongs to. The model showed a precision and recall of 76% on average for each class, indicating that the model was reasonably effective in identifying pipeline damage instances caused by natural forces across all classes. The precision and recall values provide insights into the model's ability to accurately predict positive instances and capture all relevant instances, respectively. Overall, the results from the KNN algorithm with the grid search method demonstrate that the KNN model, with the identified optimal hyperparameters, can classify pipeline damage instances with acceptable accuracy.