## 4 Bigram and Traditional Results
### Advanced Leak Detection in Water Networks
#### 4.4.2 Traditional Analysis  
In Table 4, a summary of the articles mainly identified with the topic of leak detection is displayed. It is observed that classification models are primarily constructed, with accuracy being the main metric used for evaluating the models. Among the principal techniques found are RF, ANN, SVM, and KNN. Additionally, convolutional networks were used for constructing the models in the studies that utilized images or multivariate time signals. The primary issue to address was leak detection; however, some works also considered leak localization. A brief summary of some of the referenced works in the table follows. Figure 14: Conceptual structure and dendrogram for a leak detection topic. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Techniques** & **Metrics** & **Data** & **Application** \\ \hline DDSCAN, CNN & Mean Per-Class Error = 0 & The data source for leakage detection in this paper is derived from hydraulic simulations. & To tackle the issue of water leakage in urban water supply networks, which affects water quality, hydraulics, and public health [78]. \\ \hline ANN & Distance leak error \(<\) 2.32 for 95\% of points. & Transient head traces at the valve after its closure & To introduce a novel methodology for identifying features in water pipelines, which, by accurately predicting the presence of junctions and leaks, aims to enhance the assessment and maintenance of water distribution systems [79]. \\ \hline SVM & Accuracy between 80 and 83\% for the testing dataset. & Wireless sensors networks was presented + 4G for transmission & To develop an efficient water pipeline monitoring system using wireless sensor networks and SVM-based leakage identification to conserve resources and minimize economic losses [80]. \\ \hline LDA, ANN & Accuracy 80\%, for the testing dataset. & Pressure data & Introduce a data-driven approach using limited pressure measurements and machine learning classifiers to accurately localize leaks in water distribution networks, ultimately conserving water resources and reducing costs [81]. \\ \hline Time-frequency CNN & Accuracy 99\%, for the testing dataset. & Real datasets from Chengdu city and synthesized datasets containing Gaussian white noise & To propose a leakage spectrogram and time-frequency convolutional neural network model for improved accuracy and stability in leak detection, and to compare its performance with other classification models under various signal-to-noise ratio conditions [82]. \\ \hline GBT & Accuracy, 99.8\%, for the testing dataset. & Several months from multiple cities across North America. & To provide an overview of the challenges faced by water utilities in detecting and managing leaks in aging water infrastructure and to present various technologies and methods that have been developed to address this issue [83]. \\ \hline RF & Accuracy in different context. The mean of accuracy 33.5 \% & The dataset includes 24 features as inputs to each mode. The leak shapes were divided into five separate datasets based on their leak area. & To present a methodology for predicting leak shapes using vibration signals and introduce an innovative signal processing technique that combines machine learning methods, specifically Random Forest classifiers, with various signal features [4]. \\ \hline RF, Dtree, KNN, Naive Bayes & Accuracy, Precision, Recall, F1-score. 10 scenarios were significantly, where the best algorithm was Bayes, obtaining an average of 96.27, 95.94, 95.78, 93.80. & Viens company dataset that describes the water distribution networks of the water distribution networks of theaters and locations, as well as identifying the volume of losses material in Water Distribution Networks [85]. \\ \hline \end{tabular}
\end{table} 
Table 4: Summary of applications and techniques in water leak detection. Random Forest (RF), artificial neural network (ANN), convolutional neural network (CNN), Decision Tree (DT), and support vector machine (SVM).