## 5 Discussion  
An accurate water supply condition detection system should be based on efficient and fast leakage diagnosis as well as the prediction and estimation of the probability of future events of this type. The problematic issues are the drawbacks of specific methods for the detection of water supply network operating conditions, which cannot always be meaningfully implemented in every water supply system. Non-acoustic methods, such as tracer injection, are characterized by high costs but have the advantage of high leak detection and the possibility of locating leaks in pipes of deeper locations. The thermal imaging method can only be used to locate leaks at times when the temperature of the water flowing in the water supply system is higher than the ambient temperature. This method is particularly suitable for detecting pre-failure conditions in water supply networks during winter and spring. Temperature drops and ground freezing in winter are causes of water supply failures that become apparent after the frost has subsided. Fittings installed on water supply lines, such as gate valves and hydrants, are particularly vulnerable to this type of failure [39]. The strengths of the GPR method are the ability to detect water leaks in pipes not only made of metal and the possibility of performing tests at a speed of up to 15-30 km of network per hour. The main disadvantage is the difficulty of selecting the appropriate wave frequency, which depends primarily on the composition and structure of the ground medium. In urban agglomerations, the amount of underground infrastructure and the variability of the structure of the topsoil, due to the fact that it is anthropogenic soil, is the cause of numerous false alarms of leaks in places where they do not occur [69; 70]. On the other hand, acoustic methods are time-consuming and costly and do not produce satisfactory results for heavily corroded or sediment-covered pipelines. The resulting turbulence due to the increase in the roughness of the inner walls of the pipelines strongly affects the recorded sound intensity levels [38]. Due to the severe limitations of the aforementioned hardware methods, the scientific community is focusing more attention on the implementation capabilities of software methods. Acoustic signal analysis, due to the novelty it represents, is mainly implemented on a semi-technical scale (small-scale prototype). This solution is expensive because to achieve a satisfactory effect, it requires the installation of multiple sensors located across short distances. Moreover, it is necessary to apply signal filtering, which will get rid of noise masking the leakage. The biggest inconvenience of using, for example, wavelet transform for the detection of abnormal operating conditions of technical infrastructure is the necessity of fitting an appropriate wavelet function to the vibration distribution of acoustic waves. Its shape is a key issue in the later reconstruction of the signal in a time-frequency domain. Another disadvantage is the selection of the number of signal decompositions. The more of them there are, the better measurement noise is eliminated, but at the same time, more individual and local signal characteristics can be lost. Moreover, the filtering of measurement data alone is not sufficient; it is necessary to use a classifier of network operating states. Noise extraction using wavelet transform can result in the attenuation of acoustic phenomena accompanying a pipeline being torn apart as a result of a failure [33]. This implies the loss of relevant information about the event along with noise separation and rejection. However, when combined with computational intelligence methods, acoustic signal analysis allows for the accurate diagnosis of the operating conditions of a water supply network [48]. All acoustic methods are affected by noise from the external (urban) environment [38]. It is, therefore, required to know the acoustic background and noise level maps before analyzing such signals and to look at the noise level maps for the area.