No abstract

No abstract

Movable weirs have been developed to address the weaknesses of conventional fixed weirs. However, the structures for riverbed protection downstream of movable weirs are designed using the criteria of fixed weirs in most cases, and these applications cause problems, such as scour and deformation of structures, due to misunderstanding the difference between different types of structures. In this study, a hydraulic experiment was conducted to examine weir type-specific hydraulic phenomena, compare hydraulic jumps and downstream flow characteristics according to different weir types, and analyze hydraulic characteristics, such as changes in water levels, velocities and energy. Additionally, to control the flow generated by a sluice gate, energy dissipators were examined herein for their effectiveness in relation to different installation locations and heights. As a result, it was found that although sluice gates generated hydraulic jumps similar to those of fixed weirs, their downstream supercritical flow increased to eventually elongate the overall hydraulic jumps. In energy dissipator installation, installation heights were found to be sensitive to energy dissipation. The most effective energy dissipator height was 10% of the downstream free surface water depth in this experiment. Based on these findings, it seems desirable to use energy dissipators to reduce energy, as such dissipators were found to be effective in reducing hydraulic jumps and protecting the riverbed under sluice gates. OPEN ACCESSWater 2015, 7 5116

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.

No abstract

No abstract

It is important to manage leaks in water distribution systems by smart water technologies. In order to reduce the water loss, researches on the main factors of water pipe network affecting non-revenue water (NRW) are being actively carried out. In recent years, research has been conducted to estimate NRW using statistical analysis techniques such as Artificial Neural Network (ANN) and Principle Component Analysis (PCA). Research on identifying factors that affect NRW in the target area is actively underway. In this study, Principle components selected through Multiple Regression Analysis are reclassified and applied to NRW estimation using PCA-ANN. The results show that the principal components estimated through PCA are connected to the NRW estimation using ANN. The detailed NRW estimation methodology presented through the study, as a result of simulating PCA-ANN after selecting statistically significant factors by MRA, forward method showed higher NRW estimation accuracy than other MRA methods.

No abstract