Leaks in a water distribution network (WDS) constitute losses of water supply caused by pipeline failure, operational loss, and physical factors. This has raised the need for studies on the factors affecting the leakage ratio and estimation of leakage volume in a water supply system. In this study, principal component analysis (PCA) and artificial neural network (ANN) were used to estimate the volume of water leakage in a WDS. For the study, six main effective parameters were selected and standardized data obtained through the Z-score method. The PCA-ANN model was devised and the leakage ratio was estimated. An accuracy assessment was performed to compare the measured leakage ratio to that of the simulated model. The results showed that the PCA-ANN method was more accurate for estimating the leakage ratio than a single ANN simulation. In addition, the estimation results differed according to the number of neurons in the ANN model’s hidden layers. In this study, an ANN with multiple hidden layers was found to be the best method for estimating the leakage ratio with 12–12 neurons. This suggested approaches to improve the accuracy of leakage ratio estimation, as well as a scientific approach toward the sustainable management of water distribution systems.

The non-revenue water (NRW) ratio in a water distribution system is the ratio of the loss due to unbilled authorized consumption, apparent losses and real losses to the overall system input volume (SIV). The method of estimating the NRW ratio by measurement might not work in an area with no district metered areas (DMAs) or with unclear administrative district. Through multiple regression analyses is a statistical analysis method for calculating the NRW ratio using the main parameters of the water distribution system, although its disadvantage is lower accuracy than that of the measured NRW ratio. In this study, an artificial neural network (ANN) was used to estimate the NRW ratio. The results of the study proved that the accuracy of NRW ratio calculated by the ANN model was higher than by multiple regression analysis. The developed ANN model was shown to have an accuracy that varies depending on the number of neurons in the hidden layer. Therefore, when using the ANN model, the optimal number of neurons must be determined. In addition, the accuracy of the outlier removal condition was higher than that of the original data used condition.

The non-revenue water (NRW) ratio in water distribution networks is the ratio of losses from unbilled authorized consumption and apparent and real losses to the total water supply. NRW is an important parameter for prioritizing the improvement of a water distribution system and identifying the influencing parameters. Though the method using multiple regression analysis (MRA) is a statistical analysis method for estimating the NRW ratio using the main parameters of a water distribution system, it has disadvantages in that the accuracy is low compared to the measured NRW ratio. In this study, an artificial neural network (ANN) was applied to estimate the NRW ratio to improve assessment accuracy and suggest an efficient methodology to identify related parameters of the NRW ratio. When using an ANN with the optimal number of neurons, the accuracy of estimation was higher than that of conventional statistical methods, as with MRA.

Quantitative flood frequency investigation in a large estuary is somewhat challenging by numerical modelling, because the model optimization depends on the appropriate physical and hydrodynamic properties of the estuarine river. This study attempts to solve the bathymetry configurations of the Meghna River estuary and the assimilation of flow data, which exposed an important role in water level prediction. Upstream flow rates and nonlinear semidiurnal tides have an impact on the instability of the flow in this estuarine river. A large amount of flow accumulates in the upstream confluence against or in favor of tides during the rainy season from the adjacent river basins and significantly moves in the Bay of Bengal. The aim of this study is to predict water levels in the un-gauged regions of the Meghna River estuary. A numerical technique was developed using Mike21 flexible mesh, comprising shallow water hydrodynamic components in the estuary. Subsequently, log-normal distribution was employed to analyze the flood magnitudes among the ungauged stations of the estuary. The calibration results comprised with the observed water levels adequately. In conclusion, these water level prediction results can be applied to alleviate the coastal land from extreme flooding and to design hydraulic structures in the narrow streams.

This paper aims to show how numerical modelling based on 2D SWE can be used to analyze the cleaning effectiveness of flushing waves in storm tanks. The case study under consideration is an existing storm tank located in Badalona, a municipality of Barcelona, Spain. Storm tank cleaning systems are critical features that must be carefully addressed. If not appropriately addressed, operation and maintenance work costs can drastically increase. There are numerous currently available technologies for cleaning storage tanks. However, no specific guide on this field has been identified. References are provided by the manufacturers through their commercial catalogues. Generally, this information is not based on experimental or numerical experiences or results have not been published in the literature of scientific papers. In this study, a public domain software (IBER) was used to develop 2D hydraulic analysis of the selected tank. The results obtained show how the phenomenon of recirculation is acting in some areas of the lane. This implies a dissipation of energy, thus causing difficulties in terms of cleaning procedures. Furthermore, two new scenarios have been tested to determine how a different lane width might affect hydrodynamic behavior. A newly suggested geometry for the existing lane of the tank is proposed by using the numerical modeling software. The proposed geometry in the current pilot tank achieves higher velocities and avoids recirculation areas. The results demonstrate that numerical modelling of these types of processes is possible with the computer models available (commercial codes) and can be used to optimize cleaning system design.

Recently, the number of urban river restoration projects that use artificial spurs to create ecologically sound rivers has steadily increased. In this study, the effect of spur placement on improving the ecological environment of rivers is investigated for an optimal riverbank spur design based on small-scale experimental results. The experiments were conducted by varying the placement of the spur formations, the spur types, and the dimensionless Froude Numbers. Stone mattress, wickerwork, and combinations of these materials were used as spurs. The experimental results show that the spur placement helped create a gentle meandering river and secured the fish habitat. A combination of stone mattress and wickerwork (wickerwork beneath a stone mattress) resulted in the most effective type of spur for achieving a gentle meandering and sound fish habitat. The average water flow velocities in front of the spurs decreased by roughly 30%, while the average water flow velocities behind the stone mattress decreased by approximately 70%. Consequently, it is concluded that the well-implementation of spurs could induce an ecologically favorable habitat for fish and fish spawning.