## 4 Bigram and Traditional Results
### Advanced Leak Detection in Water Networks
#### 4.4.2 Traditional Analysis
\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 RF & Accuracy 95\% & The data was collected using 18 pressure sensors and 3 flow sensors, with a noise level of 1.5\% MPI & To propose a method for improving the accuracy of a classifier model for leak location in water distribution networks [86]. \\ \hline KNN & Accuracy 52\% & The dataset is created through a two-step process involving hydraulic transient simulations of a water network. The dataset consists of pressure head data from all nodes in the network. & A novel method for identifying and locating leaking pipes in pressurized water distribution systems using transient modeling and the K-nearest neighbors (K-NN) algorithm [87]. \\ \hline RF, ANN, SVM & Accuracy 75\% & Data from smart meters located alongside water distribution pipelines + Narrow-Band IoT & To introduce the concept of the Internet of Things in water management. It discusses the challenges and limitations of traditional methods of leak detection in pipes and highlights the potential of using a low-cost sensor network and machine learning algorithms to monitor and control water leaks more efficiently [88]. \\ \hline DT, KNN, RF, Adaboost & Accuracy: The most outstanding result was achieved by the Random Forest (RF) model, which demonstrated a 100\% accuracy rate. & Used the radial sensing direction for signal collection with a sampling rate of 300 samples/s in streaming mode. & To introduce the use of cost-effective MEMS-based accelerometers for leak detection and explain the methodology used, including experiments on real networks, data analysis, and the development of machine learning models [89]. \\ \hline SVM & Accuracy between 88 and 93\% & Vibrations measurements using low-power accelerometers & To develop, test, validate, and demonstrate a machine-learning-based risk assessment method for early detection of leaks with high likelihood, their good location, and accuracy assessment in the water distribution system at the University of Luc 5 UNISRE demonstration site in France [90]. \\ \hline ANN & Accuracy 97\% & Flow data set for pipe & Implementing a machine learning-based risk assessment method enables quick detection of highly probable leaks, precise geolocation, and accurate evaluation within the water distribution system [91]. \\ \hline ANN, RF, DT, Logistic Regression & Accuracy, Precision, Recall 100\%. & Flow and pressure data were determined using EPANET software infrastructure damage, and soil contamination caused by water leakage [92]. \\ \hline ANN, DT, SVM, KNN & Accuracy 100\% in the best cases for both metal and non-metal pipes. & Features extracted from de-noised signals of sound & Leaks in water distribution networks [93]. \\ \hline \end{tabular}
\end{table} 
Table 4: _Cnt._ \begin{table}
\begin{tabular}{l l l l} \hline
\begin{tabular}{l} \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 4: _Cont._In [98], a state-of-the-art review is conducted, focusing on the escalating demand for water due to socio-economic changes and the notable losses from leakages in water distribution systems. Current leak detection methods, which are labor-intensive and time-consuming, are scrutinized along with the historical evolution of leak management technologies. The burgeoning role of sensor-based smart water technology is underscored. This study's objective is to impart insights and guide future research toward effective strategies for leak monitoring, detection, and assessment. A methodology that employs artificial neural networks (ANN) and fluid transient waves for the non-invasive assessment of water pipe condition is introduced in [79]. The technique allows for predicting elements in a pipeline, such as the location and properties of a junction and the identification of leaks. The results reveal that the developed ANN can predict a junction with an error of 2.32 m or less in 95% of the cases.