## 5 Discussion
An additional disadvantage is that plastic pipelines transmit sound waves less than metal pipelines. Satisfactory results of this type of method are achieved only when a dense network of sensors is used, which increases costs, and the selection of an appropriate sensor placement technique can be a key issue in the subsequent analysis of the signals coming from them [67]. The interpretation of sound wave parameters gives satisfactory results most often when straight sections of ducts are considered. Any changes in the direction cause the attenuation of acoustic phenomena, making the signals weaker [71]. All the above-mentioned limitations of the methods analyzing acoustic signals mobilize the scientific community to select more modern techniques of the processing and use of time series of network operating parameters, such as flow rate and/or pressure [72]. Despite the fact that attempts are made to test more advanced methods (e.g., based on the analysis of the hydraulic shock transition wave) in the conditions of an exploited water supply network, these methods are rarely implemented permanently into professional (operational) practice. [PERSON] et al. [20] highlighted the problem of too much data being produced by traditional methods and not being properly interpreted afterwards because their time-consuming analyses are too expensive. A shortcoming of the methods based on short-term transients is the need to determine the roughness coefficient, which is the primary cause of attenuation of the hydraulic shock wave. The estimation of its value is a very complicated task under real operating conditions. Most methods work much better for large outflows from water supply systems because the effect on flow or pressure parameters is more noticeable. Research results suggest that large outflows are much easier to detect because the \"bending\" of the curve showing pressure and pressure drops are more visible [20]. One of the most frequently cited drawbacks of transient analysis is the limited number of experiments conducted in operational conditions of network operations and only on laboratory systems, which reduces the implementation reliability of these methods and idealizes the test conditions [13; 25]. The reason for this is the inability to simulate short-term transients on an operational, real-world network due to the safety of these systems. Such experiments could cause duct damage and water contamination resulting from backflow [73; 74]. Another issue with measurement methods is the proper location of the sensor so that a transition wave of significant power is recorded [15; 51]. Too much distance causes attenuation of the wave along its length due to friction (roughness of the duct), so that its energy is dissipated and, consequently, it may not be captured by the sensor. [PERSON] et al. [75] showed that the main disadvantage of measurement indirect methods is the dependence of their effectiveness on the location and number of measurement devices. The best effect of detecting the operating conditions of a water supply network using hydraulic modeling can only be obtained by properly calibrating it and using machine learning methods simultaneously [76]. This is evidenced by the results obtained by combining hydraulic modeling with deep neural networks, during which 90% leak detection accuracy was obtained. Although methods based on hydraulic modeling are among the cheapest, they require faithful calibration to obtain reliable results. This requires the use of a densely deployed sensor monitoring network, which is a guarantee of good model quality. Another issue is the use of emergency historical data to create algorithms for warning against abnormal conditions in the future; each uncontrolled water leak has its own specific distribution that affects the characteristics of the pipeline. It does not always generate an unambiguous response in time series. It is also important to estimate the size of the water demand for individual consumers.