###### Abstract
At the same time, the data collected by infrared thermography and CCTV are two-dimensional and can be applied directly by two-dimensional CNNs to detect a leak. [PERSON] et al. detected a pipeline defect using videos collected from a camera installed in the pipeline robot. The video was converted into grayscale, fed to the VGGNet for feature extraction, and the extracted features were then classified using a SVM [32]. [PERSON] et al. proposed an architecture to detect a leak in a water pipeline using an ensemble 1 DCNN-SVM with accelerometer data [25]. The 1 DCNN performed the feature extraction process, and the SVM tailored in the 1 DCNN detected the leak with great accuracy. [PERSON] et al. implemented a leak detection mechanism using a 1 DCNN with pressure data [36]. The 1 DCNN detected the leak with 95% accuracy. Further, [PERSON] et al. implemented a leak detection system using a 1 DCNN with pressure sensor data. The 1 DCNN detected the leak with a 0.59% average error [37]. [PERSON] et al. implemented a mechanism to detect and classify leaks in water pipelines using pipeline vibrations [42]. The observed accuracy of leak detection and classification using the CNN was 95.8%. A few methods that help to convert one-dimensional data to two-dimensional are mel-frequency cepstral coefficients (MFCCs) [10; 14; 17], Bartlett beamforming [43], Hilbert-Huang transform (HHT) [16], continuous wavelet transform (CWT) [8], scaleogram [8], short-time Fourier transform (STFT) [22], and spectrogram [23]. The MFCC is widely used in speech recognition to identify the speaker and tone. It helps capture the essential frequency parameters from the data. Using acoustic signals, [PERSON] et al. implemented a MFCC and 2 DCNN to detect underground water pipelines and achieved good accuracy [10]. Similarly, [PERSON] et al. implemented MFCC and 2 DCNN to detect gas pipeline leaks using acoustic emission data. The paper aims to improve the signal-to-noise ratio by introducing a discrete stochastic resonance approach [14]. Using a convolutional neural network, [PERSON] et al. implemented an AI-based leak diagnostic instrument for water pipelines [17]. The leak diagnostic module consisted of an acoustic vibration sensing unit and a communication module to capture the audio data. The communication module helped store the data in the cloud. The CNN helped detect the leak after extracting essential features using MFCC. With MFCC and CNN, the observed leak detection accuracy was 95%. A few pipeline leak localization methods available are cross-correlation (CC) [44; 45; 46; 47; 48; 49; 50] and generalized cross-correlation (GCC) [11; 12; 13]. CC and GCC localize the leak based on time difference estimation. So, CC and GCC need two sensors to localize a leak. The data collected from the pipeline using sensors are noisy. A few noise removal methods used for noise removal are empirical mode decomposition (EMD) [11], independent component analysis (ICA) [12; 13], and wavelet packet [13; 49]. [PERSON] et al. implemented a leak localization method using wavelet packet analysis and CC [49]. With this method, the achieved localization error was less than 8%. Another leak localization method implemented by [PERSON] et al. combined adaptive ICA with GCC using two acousto-optic sensors' data and achieved an accuracy of 93% [12]. Further, to improve localization accuracy, [PERSON] et al. introduced EMD-ICA and GCC using two acousto-optic sensors' data and achieved an accuracy of 96.47% [11]. Furthermore, to improve the localization accuracy using two acousto-optic sensors' data, [PERSON] et al. implemented a wavelet packet-ICA-based GCC and achieved improved accuracy [13]. Research gaps and key findings identified in the literature survey are:  
* The ML and DL algorithms have been explored only for pipeline leak detection with various sensor inputs. * Even though the ML and DL have shown good leak detection accuracy, they have not been applied to localize leaks.