## 2 Related Work  
This section introduces a factor-analysis-based feature-selection method related to the proposed method. Researchers have studied a pilot project of this mapping approach to explore the first step in understanding the effects of NG leaks [9; 11]. NG masses were measured using a Picarro CH4 sensor and a Google Street view machine [11]. This refers to gas sensors that are resistant to fire and wired and wireless transmitters that can be used in high-sensitivity facilities. In addition, the vehicle used is an IoT-basedremote monitoring system, with a dual-antenna diagnostic solution used for real-time data aggregation analysis. Further, we present a list of environmental and gas features in raw data properties of NG found in mobile-device-based methane gas research [12]. When NG is released into the air, it creates major climate and environment problems. The fluctuations in the sensitivity of the devices used to measure the CH4 level are relatively high. One study is related to the present study [13], where ordinal-encoder (OE) normalization was used as the target feature of CH4 (g min). Then, k-means clustering for the labeling of CH4 for the data pre-processing part was used. The earlier work [9; 11] sourced the flow rate of the CH4 leaks. A machine-based approach to environmental engineering has been widely used to predict natural gas leaks [14; 15]. Natural gas pipelines were subjected to real-world acoustic emission signal coefficient and cluster analyses [16]. A comprehensive review and a model for predicting mercury emissions based on the coal supply characteristics and operating conditions have been discussed [17]. Researchers used factor analysis to reduce the high-dimensional variables' size and obtain the optical properties during the cluster analysis [18]. As mentioned above, we also compared the results of the proposed method with several statistical studies based on other similar gas data. However, no other researchers have utilized machine-learning algorithms on these data types before. Using a comprehensive database, factors such as various pressures, temperatures, and composition ranges successfully implement natural gas pressure and emissions as an intelligent model for predicting evolution [19]. Some authors have proposed systems for detecting leaks in water and natural gas networks in residential and construction environments [20]. The Gaussian mixed model (GMM), the hidden Markov model (HMM), and the one-class support vector machine (OC-SVM) were validated according to comparative perspective statistical modeling, and the features were selected by modifying the sequence character selection (SFS). The best result was 95.32% for the hourly and daily features for both the GMM and HMM on the Almanac of Minutely Power dataset (AMPds) [21] and the Department of International Development (DFID) [2] datasets. Standard machine-learning techniques are divided into supervised, semi-supervised, and unsupervised. The data we use are unsupervised data without labels. XGBoost is widely regarded as flexible as it contains evaluation functions and a variety of optimization options. It can also utilize tree pruning using the Greedy algorithm, which makes it powerful for overfitting. With a special connection to other algorithms and excellent utility, it is capable of ensemble learning, which is another strength [22]. When creating a tree, XGBoost uses CART (classification and regression). All leaves are related to the model's final score, unlike the decision-making tree that only considers the result values of leaf nodes. The purpose of this study was to compare the XGBoost, KNN, DT, RF, NB, and MLP [4; 10; 20; 23; 24; 25; 26] classification techniques for feature selection using factor analysis (FA) [5] and OE normalization for risk predictions of NG leaks. The system identifies pipeline gas leaks with an input vector based on the Manhattan distance of certain features by exploiting the k-nearest neighbor classifier.