## 4 Leak Detection Technologies
### Software-Based Methods
#### 4.2.6 Deep Learning  
Deep learning techniques, particularly artificial neural networks (ANNs) and convolutional neural networks (CNNs), have become useful tools for enhancing the detection and localization of water leaks in distribution networks [84; 85]. Artificial neural networks (ANNs) have recently been employed to detect and locate leaks in water supply networks [86]. The ANN establishes a relationship between input and output data. In the case of water networks, input data characterize the normal and abnormal states of the system, with and without leaks. The ANN acts as a classifier to estimate the current state of the system and identify the leaks based on available data [45]. The ANN method has been used to detect and localize leaks based on flow and pressure data. It effectively detected bursts in a real case study of 144 zones in the United Kingdom [87]. Furthermore, the ANN model can be integrated with Geographic Information Systems (GIS) to assess water leakage and prioritize pipeline replacement [23]. [PERSON] et al. [88] utilized data collected over 14 years to predict pipe failures in the water supply system of Wattrelos City in France using an ANN method. The model also determined the optimal time for pipe renovation in the system [45; 83]. While ANNs have showcased significant promise in leak detection, certain limitations must be highlighted. These include the necessity for extensive, high-quality training datasets, challenges in model interpretability, and computational resource requirements. To drive future research in this domain, it is imperative to focus on refining training strategies, enhancing model robustness across diverse operational conditions, and exploring techniques for improving model interpretability and human-AI collaboration in leakage management. Convolutional neural networks (CNNs) are particularly useful in analyzing complex datasets, such as acoustic emissions and pressure residual maps. Unlike ANNs, which rely on structured numerical input, CNNs process more complex data representations, making them ideal for applications such as acoustic-based leak detection. Several studies have employed CNNs to convert pressure maps into images, enabling models to identify leaks with high precision by detecting subtle changes in pressure or acoustic patterns. For instance, one study used CNNs to analyze acoustic wave files collected from a smart water network in Adelaide, Australia, achieving an accuracy of over 90% in distinguishing leaks from background noise [86]. [PERSON] et al. [89] explored deep learning for leak localization using CNNs. The study introduced a novel approach that converted pressure residual maps into images, applying CNNs to identify leak locations with high accuracy. This method used pressure data from a District Metered Area (DMA) in Hanoi, Vietnam, and demonstrated strong generalization by applying Bayesian reasoning over time to handle uncertainty and noise. The study reported a significant classification accuracy, showcasing how CNNs can process pressure data for leak detection in real-world scenarios. [PERSON] et al. [90] focused on using CNNs for leak detection based on actual leak sounds collected from real-world pipelines. The study utilized recurrence plots to transform time-series acoustic data into 2D images that could be processed by the CNN model. The model achieved a detection accuracy of over 80% across 15 of the 20 leak cases tested, demonstrating that acoustic-based CNN models could effectively differentiate between leak sounds and background noise. This method was validated with field data, making it particularly valuable for early leak detection in practical settings. The combination of ANNs and CNNs has enabled real-time, automated leak detection, significantly reducing the need for manual inspections and minimizing water losses. However, challenges remain, including the need for large, high-quality datasets to train these models and the computational resources required for deploying them in real-world systems.