Flash floods in mountainous catchments are often caused by the rainstorm, which may result in more severe consequences than plain area floods due to less timescale and a fast-flowing front of water and debris. Flash flood forecasting is a huge challenge for hydrologists and managers due to its instantaneity, nonlinearity, and dependency. Among different methods of flood forecasting, data-driven models have become increasingly popular in recent years due to their strong ability to simulate nonlinear hydrological processes. This study proposed a Support Vector Regression (SVR) model, which is a powerful artificial intelligence-based model originated from statistical learning theory, to forecast flash floods at different lead times in a small mountainous catchment. The lagged average rainfall and runoff are identified as model input variables, and the time lags associated with the model input variables are determined by the hydrological concept of the time of response. There are 69 flash flood events collected from 1984 to 2012 in a mountainous catchment in China and then used for the model training and testing. The contribution of the runoff variables to the predictions and the phase lag of model outputs are analyzed. The results show that: (i) the SVR model has satisfactory predictive performances for one to three-hours ahead forecasting; (ii) the lagged runoff variables have a more significant effect on the predictions than the rainfall variables; and (iii) the phase lag (time difference) of prediction results significantly exists in both two- and three-hours-ahead forecasting models, however, the input rainfall information can assist in mitigating the phase lag of peak flow.

Isolation valves are crucial components of water distribution systems for separating pipe segments from the network for repair or maintenance purpose. This paper looks at the impacts of isolation valve failure on three indictors, including number of valves needed to isolate a distribution system segment, size of distribution system segments, and shortfall in meeting demands during failure. A network with various isolation valve configurations in terms of valve density is used as a case study. The results obtained from the case study show that the failure of an isolation valve has substantially varying impacts on system performance during a shutdown. The valve density in the network determines the impacts of inoperable valves on a shutdown. Generally speaking, a higher density of isolation valves leads to the less impact of valve failure. Finally, several conclusions drawn from the critical valve analysis in this study can be applied to guide isolation valve maintenance and management.

Because of the large computational burden associated with the direct assessment of reliability, the indirect indices of reliability have recently received more attention in the framework of water distribution system design. Two new energy-related indices for reliability evaluation (i.e., available power index and pipe hydraulic resilience index) are developed in this paper. The performance of these new indices is evaluated and compared with that of four existing indices (three other energy-related indices—i.e., resilience index, network resilience index, and modified resilience index—and the entropy-based method, i.e., diameter-sensitive flow entropy) according to the following two-step methodology. In the first step, the application of the multiobjective optimization makes it possible to determine optimal network configurations that trade-off the installation cost (to be minimized) against the generic indirect reliability index (to be maximized). In the second step, the performance of the optimal solutions in terms of explicit reliability assessment is examined under conditions in which the original network is perturbed by applying demand variations and random pipe failures to account for future operating uncertainties. The Hanoi and the Fossolo benchmark networks are used as case studies. The results obtained show that energy-based indices yield an overall superior estimate of reliability in comparison with the diameter-sensitive flow entropy. Furthermore, the new indices show some advantages in the evaluations performed under demand and pipe failure uncertainties.

With the development and industrial applications of nanotechnology, increasing productions of engineered nanomaterials enter the aqueous environments by several routes (i.e. remediation purpose and unintentional release). Due to the potential risk deriving from their higher surface activity and greater transferability, engineered nanoparticles bring a new challenge for the environmental systems and human beings. Under favorable particle–particle interactions, nanoparticles aggregate, form large clusters, and settle out from the environmental solutions. The settling process can reduce the mobility of nanoparticles in environmental systems. In the current study, sedimentation experiments were conducted under different environmental conditions to investigate the settling process of nanoparticles. The results indicated that the A/A₀ ratios of titanium dioxide (TiO₂) nanoparticles increased with increasing ionic strength. They also increased near the pH_pzc (point zero of charge). However, the increasing concentrations of humic acids in aqueous solutions reduced the A/A₀ ratios even near the pH_pzc. The measured zeta potentials revealed that the TiO₂ nanoparticles became less charged in the range of pH_pzc and at higher ionic strength. It was due to the double electric layers of TiO₂ nanoparticles compressed with increasing in ionic strength and charge screening effect. Therefore, the electrostatic repulsive force between nanoparticles significantly declined, which lead to favorable aggregation. The interaction energies calculated using the DLVO theory under different pH and ionic strength conditions were consistent with the experimental results from the sedimentation experiments. In addition, the steric interactions between humic acid absorbed on the surface of TiO₂ nanoparticles were responsible for the reduction in the A/A₀ ratios.

This paper presents a comparative analysis of three partitioning methods, including Fast Greedy, Random Walk, and Metis, which are commonly used to establish the district metered areas (DMAs) in water distribution systems. The performance of the partitioning methods is compared using a spectrum of evaluation indicators, including modularity, conductance, density, expansion, cuts, and communication volume, which measure different topological characteristics of the complex network. A complex water distribution network EXNET is used for comparison considering two cases, i.e., unweighted and weighted edges, where the weights are represented by the demands. The results obtained from the case study network show that the Fast Greedy has a good overall performance. Random Walk can obtain the relative small cut edges, but severely sacrifice the balance of the partitions, in particular when the number of partitions is small. The Metis method has good performance on balancing the size of the clusters. The Fast Greedy method is more effective in the weighted graph partitioning. This study provides an insight for the application of the topology-based partitioning methods to establish district metered areas in a water distribution network.

Solute mixing at the cross junction was assumed to be instantaneous and completely mixed in initial water quality models of water distribution system. With recent developments, some computational simulations and experiments verified an incomplete mixing state at cross junctions, which affected the accuracy of water quality model in water distribution networks. This paper, by the theory of computational fluid dynamics, confirms incomplete mixing at the pipe intersections. At the cross of pipe network, the forward direction of the fluid particle is directly related to water momentum. A mixing model at the cross in water supply networks is established to improve the water quality model and the simulation accuracy. For the potential of accidental or intentional contamination events, it makes a powerful prediction. This study is working to prevent and prepare for terrorist attacks, minimize public health impacts and infrastructure damage, and enhance recovery.