applied sciences Article  
Factorial Analysis for Gas Leakage Risk Predictions from a Vehicle-Based Methane Survey  
[PERSON] and [PERSON],*()  
Department of Computer Engineering, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju 28644, Korea; [EMAIL_ADDRESS] School of Information and Communication Science, Semyung University, Jecheon 27136, Korea Correspondence: [EMAIL_ADDRESS]  
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Regarding this, various studies have been conducted over the past several decades. Most gas leak detection methods can detect leaks in real-time or minutes after a leak has occurred. Leak size estimation is possible in most detection systems. Cable sensors, soil monitoring devices, ultrasonic flow meters, pressure point analysis methods, and digital signal processing do not have this ability. Technologies related to gas leak detection are mainly divided into non-technical, hardware-based, and software-based methods. Hardware-based methods generally perform better regarding sensitivity [6]. For example, a vapor-sampling system such as LEOS [7] can detect 0.05% leaks, and most non-technical optical methods can detect gas concentrations in the ppm range. A detailed overview of techniques based on signal processing, state estimation, and knowledge-based methods used for chronic leaks of onshore and offshore platforms has been discussed [8]. Data for this paper were collected via a hardware-based method with IoT-based gas detection sensors installed in a vehicle. Therefore, this study measures the data values of the elements in an environment affected by gas and predicts the level of the gas leakage risk without directly measuring the gas loss data based on the relationship between the gas data and the environmental information. These types of predictions can be made quickly using modern ML algorithms. We propose the feature selection method over natural gas leakage data based on the FA method. The novelty and distinct advantages of our research are as follows:  
* The main contribution of this paper is the novelty FA method combined with OE normalization to improve the accuracy of the results of standard ML algorithms by 6\(\sim\)10%. * Another advantage is that the data are normalized by the OE method, and logarithmic transformation is performed, theoretically resulting in a data distribution identical to that in earlier work [9]. Subsequently, imbalanced data were included in this gas data used to generate the gas leakage level given the k-mean clustering method. * In addition, the study was also implemented using actual open data that had not previously been used with the ML algorithm, which future researchers will widely use for comparative research. * It is also possible to use this method to create gas leakage data levels for air assessments in Korea. * In practice, this level is considered a step forward in forecasting to determine leakages depending on the elements of environmental data information.