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Predicting the response to rainfall in urban hydrological applications requires accurate precipitation estimates with a high spatiotemporal resolution to reflect the natural variability of rainfall. However, installing rain gauges under nearly ideal measurement conditions is often difficult in urban areas, if not impossible. This paper demonstrates the potential of deriving rainfall measurements in urban areas and bias-adjusting weather radar rainfall measurements using stormwater runoff measurements. As a supplement to point rainfall measurements from rain gauges, the developed bias adjustment approach uses catchment runoff-rainfall estimates derived from water level measurements of a stormwater detention pond. The study shows that the bias-adjusted radar product correlates highly with rain gauge measurements in the catchment. Moreover, the presented approach enables rainfall measurements within a catchment independent of rain gauges located in the catchment, making the technique highly applicable for increasing the density of ground observations and thus improving weather radar precipitation estimates over urban areas. The method also derives the catchment-specific runoff coefficient independently of expensive flow measurements in the catchment, making the method very scalable. This paper highlights the potential of using easily achievable catchment runoff-rainfall measurements to increase the density of available ground observations and thereby improve weather radar precipitation estimates. HIGHLIGHTS Including stormwater pond-derived rainfall improves radar QPE.; Stormwater runoff measurements complement rain gauges and can be used as opportunistic rainfall measurements for radar adjustment.; Catchment runoff-rainfall derived from stormwater ponds can increase the spatial density of ground observations for radar adjustment.; The runoff coefficient can be derived in catchments without rain gauges from water level measurements in stormwater ponds.;

This paper presents the benefits of using mathematical optimization for reservoir operation based on the assumed availability of short-term runoff forecasts. The novelty is the inclusion of the SSARR hydrological routing as optimization constraints in multiple time step optimization, where the routing coefficients are adjusted dynamically as functions of the channel flows. The paper shows significant reduction of downstream peak flows in flood-prone areas even with a forecast horizon of only 2 days, and it also includes the results of testing the effects of different lengths of forecasting horizons on model results. The case study is conducted on the Damodar River Basin in the Indian State of West Bengal, where basin development started in the 1950s, with flood protection of the downstream river valley as the highest management priority, in addition to water supply and hydro power. The solution methodology and the model results presented in this paper pave the way for eventual introduction to automated management of reservoir outflows that could shape water resources industry in much the same way that auto-pilot and driverless cars are revolutionizing the transportation industry, assuming that runoff forecasting capabilities continue to improve. HIGHLIGHTS Use of linear programming guarantees finding global optimums for large multi-reservoir systems.; Hydrological channel routing embedded in the optimization solver adjusts the routing parameters based on optimized channel flows.; The paper demonstrates the capabilities of the WEB. BM model which can be accessed and tested by any interested practitioner free of charge.;

In the context of water distribution networks (WDNs), researchers and technicians are actively working on new ways to transition into the digital era. They are focusing on creating standardized methods that fit the unique characteristics of these systems, with a strong emphasis on developing customized digital twins. This involves combining advanced hydraulic modeling with advanced data-driven techniques like artificial intelligence, machine learning, and deep learning. This paper begins by giving a detailed overview of the important progress that has led to this digital transformation. It highlights the potential to create interconnected digital water services (DWSs) that can support all aspects of managing, planning, and designing WDNs. This approach introduces standardized procedures that allow a continuous improvement of the digital representation of these networks. Additionally, technicians benefit from DWSs developed as QGIS software plugins. These services strategically enhance their understanding of technical decisions, improving logical reasoning, consistency, scalability, integrability, efficiency, effectiveness, and adaptability for both short-term and long-term management tasks. Notably, the framework remains adaptable, ready to embrace upcoming technological advancements and data gathering capabilities, all while keeping end-users central in shaping these technical developments. HIGHLIGHTS The paper explores the digital transformation in water distribution network management.; It emphasizes the opportunities offered by ICT/IoT and GIS/BIM tools, hydraulic modeling advancements, and AI algorithms for planning and management tasks.; The paradigm is implemented as digital water services, enhancing engineering awareness and supporting management activities.;

This paper proposes a novel energy-balanced numerical scheme for the two-layer shallow water equations (2LSWEs) that accurately captures internal hydraulic jumps without introducing spurious oscillations. The proposed scheme overcomes the problem of post-shock oscillations in the 2LSWE, a phenomenon commonly observed in numerical solutions of non-linear hyperbolic systems when shock-capturing schemes are used. The approach involves reconstructing the internal momentum equation of 2LSWEs using the correct Hugoniot curve via a set of shock wave fixes originally developed for single-layer shallow water equations. The scheme successfully preserves all stationary solutions, making it highly suitable for simulations of real-life scenarios involving small perturbations of these conditions. HIGHLIGHTS The paper proposes a remedy to the problem of post-shock oscillations in the two-layer shallow water equations.; It is an extension of a family of well-balanced and shock-resolving schemes that were originally developed for single-layer shallow water equations.; The resulting scheme, called HLL-MSF, successfully preserves all stationary solutions, including those with shock discontinuities.;

Hydraulic engineering applications require a good knowledge of turbulent behaviour in non-prismatic channels. This paper aims to predict turbulent behaviour using large-eddy simulation (LES). The model channel has a warped transition. We perform two-phase LES of free-surface flow and validate the results using experimental data and benchmark solution. We discuss rigorous strategies for model set-up, parameter selection and parametric value assignment, including parameters in spectrum synthesiser (SS) and vortex method (VM) for inlet turbulence. The predicted flow displays complex structures due to eddy motions translated from upstream and locally generated by asymmetrical separation in the transition. The history of the flow dynamics may affect the flow development. The predicted velocity, energy spectrum, root-mean-square error, hit-rate and factor-of-two compare well with measurements and benchmark solution. Mapping mean-velocity distribution from experimental data, combined with SS, gives satisfactory inlet condition; alternatively, a 1/7th power-law for the mean-velocity, combined with VM, is acceptable. This paper uses the Okubo–Weiss parameter to delineate instantaneous coherent structures. The LES methods are reliable, efficient and cost-effective. As compared to the simulation of prismatic channels, the flow dynamics in non-prismatic channels exhibit flow separation and turbulence interactions, which increase the flow-complexity, while offering results with crucial practical applications. HIGHLIGHTS Key features of turbulence are revealed by two-phase LES of free surface flow in expansion.; Effective and practical methods for open-boundary treatments, flow initialisations and turbulence closures are explored.; Instantaneous eddy structures are delineated using the Okubo–Weiss parameter.; An upstream channel section eight times the flow depth is recommended for LES of flow in expansion.;

This paper examines a series of connected and isolated lakes in the UK as a model system with historic episodes of heavy metal contamination. A 9-year hydrometeorological dataset for the sites was identified to analyse the legacy of heavy metal concentrations within the selected lakes based on physico-chemical and hydrometeorological parameters, and a comparison of the complementary methods of multiple regression, time series analysis, and artificial neural network (ANN). The results highlight the importance of the quality of historic datasets without which analyses such as those presented in this research paper cannot be undertaken. The results also indicate that the ANNs developed were more realistic than the other methodologies (regression and time series analysis) considered. The ANNs provided a higher correlation coefficient and a lower mean squared error when compared to the regression models. However, quality assurance and pre-processing of the data were challenging and were addressed by transforming the relevant dataset and interpolating the missing values. The selection and application of the most appropriate temporal modelling technique, which relies on the quality of available dataset, is crucial for the management of legacy contaminated sites to guide successful mitigation measures to avoid significant environmental and human health implications. HIGHLIGHTS Heavy metal contamination in aquatic ecosystems is a significant environmental concern.; A series of connected and isolated lakes were examined as a model system.; A 9-year hydrological and meteorological dataset was analysed.; Multiple regression, time series analysis, and artificial neural networks were used.; The accuracy of ANN models was better than the other methods tested.;

This paper proposes a formulation of modularity tailored to the dual water distribution network (WDN) topology based on segments and valves, to be conveniently adopted for the partitioning into district-metered areas (DMAs). Notably, it allows considering both properties to be made uniform across DMAs, such as water demand or total pipe length, and properties to be made uniform inside each DMA, such as nodal ground elevations or pipe age for the sake of pressure regulation or maintenance easiness, respectively. This paper also proposes a new algorithm for the identification of the optimal clustering of WDN segments into any desired number of DMAs. Taking as a starting point any WDN clustering solution, i.e., the solution obtained with Newman's fast algorithm for community detection, the novel algorithm operates by exploring changes in the community of belonging to segments lying in the boundary between adjacent communities, by applying an optimization inspired by the simulated annealing technique. The applications of the novel modularity formulation and optimization algorithm to two case studies yield well-performing clustering solutions in terms of engineering judgment criteria, such as the low number of inter-DMA boundary pipes, uniformity of DMAs and hydraulic performance. HIGHLIGHTS The clustering of water distribution networks into district-metered areas (DMAs) is tackled in an innovative way.; An improved modularity function using the dual network topology based on segments and valves is proposed.; The uniformity of properties across and inside DMAs is considered.; A novel optimization algorithm inspired by the simulated annealing approach proves effective and efficient.;

In this paper, we examine the accuracy of estimating the hydrogeological parameters, transmissivity (T) and storativity (S), in a confined aquifer, when there are not enough available data for pumping flow rate values. While the most popular methods, used to estimate aquifer characteristics, assume that the pumping flow rate is constant during pumping, this is practically infeasible. Violation of this assumption results in errors, which are examined in this paper using field drawdown measurements. To find the aquifer characteristics, we use two methods, testing various pumping flow rates. Firstly, we employ the Cooper -Jacob equations to calculate (T) and (S) values. Afterwards, we use these values to create hypothetical drawdowns using Theis equation and finally we estimate the Root Mean Square Error (RMSE) between the actual and the hypothetical drawdowns. Then, we repeat the same process, replacing the Cooper -Jacob equations with Genetic Algorithms and Theis equation to find the aquifer characteristics by minimizing the RMSE between the actual and the hypothetical drawdowns. Although the process is applied only in three datasets, the results indicate that regardless of the method used, the obtained values of aquifer characteristics (T, S) are not considerably affected by inaccurate pumping flow rate estimations. HIGHLIGHTS Actual data were used to find how the accuracy of the pumping flow rate's estimation affects the transmissivity and storativity results.; Genetic Algorithms are applied to identify the aquifer characteristics, giving better results than Cooper-Jacob.; The estimated aquifer characteristics' error varies linearly with the estimated pumping flow rate values.;

Flow through a siphon is difficult to predict due to inherent turbulence, separation and secondary circulation. This paper overcomes the difficulty by using advanced numerical techniques and rigorously assessing their suitability. The aim of this paper is to explore reliable numerical methods for predicting submerged siphon characteristics. Using the Reynolds-averaged Navier–Stokes equations, we predicted three-dimensional velocity, pressure and turbulence quantities. We also conducted laboratory experiments for measurements of the submerged discharge coefficient Cd. The mean value of Cd predicted matches the measured mean value. The numerical results show flow separation in the siphon upper leg, causing secondary flow (SF) and increasing velocity above the crest. The SF shows complicated patterns and multiple turbulent eddies and reaches a maximum relative strength as large as 16%. The relative pressure has negative values in the crest region. The profiles of predicted longitudinal velocity in the crest region resemble the theoretical solution. The numerical methods and computation strategies from this paper are useful for investigating the performance of submerged siphons of various dimensions and/or geometric configurations under a wide range of hydraulic conditions. The RNG k-ε model is more suitable than the standard k-ε model and the Realizable k-ε model for turbulence closure. HIGHLIGHTS Submerged siphon discharge coefficient from laboratory experiments is reported.; Reliable numerical methods for predicting submerged siphon characteristics are developed.; Complex primary and secondary flows, flow separation and turbulent vortices are predicted.;

Real-time leakage detection based on pressure and flow data has become increasingly essential for water distribution systems (WDSs). Recent data-driven leakage detection approaches have largely focused on burst detection characterised as sudden outflow or sudden pressure drops but did not mention the ability to detect gradual leakage events that do not have sudden change and could cause more water loss. This study proposes an online leakage detection system based on the exponential weighted moving average (EWMA)-enhanced Tukey method to help monitor gradual leakage events of WDSs. The proposed online system comprises three main parts: data pre-processing, the online detection sub-system, and the parameter updating sub-system. The proposed online system is based on lightweight and powerful statistical tools without complex model construction. The effectiveness of the proposed system is demonstrated on leakage datasets under various real-world scenarios, including gradual leakages and bursts. The results showed that the proposed EWMA-enhanced Tukey method could detect gradual leakage events quickly while generating low false alarms. The proposed method is computationally effective and able to deal with non-stationary behaviours automatically. HIGHLIGHTS This paper proposed a leakage detection method that focused on gradual leakage events.; The proposed leakage detection method could adapt to the time-varying characteristics of flow monitoring data, such as the demand variation caused by weather-related issues.; This paper proposed a method that is robust to the data noises and could successfully detect leakage events without generating many false alarms.;

Rainfall–runoff modeling is one of the most well-known applications of hydrology. The goal of rainfall–runoff modeling is to simulate the peak river flow caused by an actual or hypothetical rainfall force. In existing methods, the rainfall–runoff relationships are quantified to predict the daily streamflow of each catchment from its landscape attributes to measure the daily rainfall. However, the structural model error, infiltration rate, and the steep slopes of the hill affect the prediction process. To tackle these issues, this paper proposed a novel rainfall prognostic model-based artificial framework, which predicts day-to-day rainfall to prevent environmental disasters. The day-to-day predictions minimize the risks to life and property and also manage the agricultural farms in a better way because the possibility of rainfall has been estimated earlier. Furthermore, the posterior fire-breathing network is utilized to estimate model errors in the computational runoff by using time-dependent and random noise to the model's internal storage to solve the uncertainty problem. Since the model errors are estimated, there are limits to the infiltration rate and thus a prophetic multilayer network is utilized which relies on the soil runoff levels. Moreover, the network measures the dynamics of soil moisture to regulate the infiltration rate according to the rural or urban section. Moreover, to measure the surface water from the steep slopes, the system offered a well-ordered selective genetic algorithm to calculate the velocity of runoff in different bend areas to overcome the numerical problem. Thus, the model results showed that the work effectively predicts the rainfall from the investigation of model errors, infiltration rates, and velocity to achieve a better prediction range in the rainfall. HIGHLIGHTS Despite the progress made in recent years, modeling hydrological reactions to rainfall prediction remains a complex task in runoff modeling. Thus the presented paper effectively introduced a rainfall prognostic artificial model framework for the prediction of rainfall.; The framework applied a posterior fire breathing network to estimate model errors with random noise to reduce the uncertainty. Further to regulating the infiltration rate, the system suggested a prophetic multilayer network which analyses the runoff levels with the soil moisture in urban and rural areas.; In addition, to evaluate the velocity at low water depths on steep slopes, the model incorporates a well-ordered selective genetic algorithm to forecast different bend areas to overcome the numerical problem. Thus, from the rainfall model, the daily rainfall is efficiently predicted to prevent the environmental glitches.; Experimental results show that the framework exhibits the highest prediction range of 12-20 mm with the subjective results, and outperforms the existing runoff models with a better infiltration rate of 1.5 cm/hr, runoff level 0.05 cm, and the obtained velocity of 0.45 mm.;

Reliable water resources management requires decision support tools to successfully forecast hydraulic data (stage and flow hydrographs). Even though data-driven methods are nowadays trendy to apply, they still fail to provide reliable forecasts during extreme periods due to a lack of training data. Therefore, model-driven forecasting is still needed. However, the model-driven forecasting approach is affected by numerous uncertainties in initial and boundary conditions. To improve the real-time model's operation, it can be regularly updated using measured data in the data assimilation (DA) procedure. Widely used DA techniques are computationally expensive, which reduce their real-time applications. Previous research shows that tailor-made, time-efficient DA methods based on the control theory could be used instead. This paper presents further insights into the control theory-based DA for 1D hydraulic models. This method uses Proportional–Integrative–Derivative (PID) controllers to assimilate computed water levels and observed data. This paper describes the two-stage PID controllers’ tuning procedure. Multi-objective optimization by Nondominated Sorting Genetic Algorithm II (NSGA-II) was used to determine optimal parameters for PID controllers. The proposed tuning procedure is tested on a hydraulic model used as a decision support tool for the transboundary Iron Gate 1 hydropower system on the Danube River, showing that the average discrepancy between modeled and observed water levels can be less than 0.05 m for more than 97% of assimilation window. HIGHLIGHTS Unreliable boundaries and initial conditions affect model-driven forecasting.; Control theory-based data assimilation (DA) is used for 1D open channel hydraulic model updating.; PID controllers, used as DA tools, must be optimally tuned.; A two-stage procedure for tuning PID controllers, using multi-objective optimization, is introduced.;

Pressurised pipe systems transport fluids daily over long distances and sediment deposits are responsible for narrowing the cross-sectional area of the pipe. This reduces the carrying capacity in gravity pipes and increases the energy consumption in rising mains. As partial blockages do not give rise to any external evidence, they are considered the most insidious fault occurring in pipe systems. Thus, the refinement of reliable techniques for detecting partial blockages at an early stage is of great interest to water utilities. This paper presents a computational fluid dynamics (CFD)-based analysis of the steady-state flow through a sharp-edged orifice which corresponds to the most straightforward partial blockage feature in a pipe. The main motivation is the fact that the interaction between pressure waves and a partial blockage – on which Transient Test-Based Techniques for fault detection are based – is strongly influenced by the pre-transient conditions at the partial blockage. The refined CFD model has been validated by considering experimental data selected from the literature. The comparison of obtained results demonstrates good performance of the numerical model. This authorised exploring in detail the features of the flow through the orifice as a necessary premise to its use within the successive transient analysis. HIGHLIGHTS A comprehensive analysis of the low-Reynolds number flow through a sharp-edged orifice (partial blockage) is provided by computational fluid dynamics (CFD).; The successful comparison of CFD with reference papers demonstrates that the model accurately describes the laminar flow through an orifice in steady-state conditions.; The obtained results can be further used for the transient analysis of the interaction between a pressure wave and the obstacle.;

Long-term inflow forecasting is extremely important for reasonable dispatch schedules of hydropower stations and efficient utilization plans of water resources. In this paper, a novel forecast framework, meteorological data long short-term memory neural network (M-LSTM), which uses the meteorological dataset as input and adopts LSTM, is proposed for monthly inflow forecasting. First, the meteorological dataset, which provides more effective information for runoff prediction, is obtained by inverse distance weighting (IDW). Second, the maximal information coefficient (MIC) can adequately measure the degree of correlation between meteorological data and inflow; therefore, the MIC can distinguish key attributes from massive meteorological data and further reduce the computational burden. Last, LSTM is chosen as the prediction method due to its powerful nonlinear predictive capability, which can couple historical inflow records and meteorological data to forecast inflow. The Xiaowan hydropower station is selected as the case study. To evaluate the effectiveness of the M-LSTM for runoff prediction, several methods including LSTM, meteorological data backpropagation neural network (M-BPNN), meteorological data support vector regression (M-SVR) are employed for comparison with the M-LSTM and six evaluation criteria are used to compare its performance. Results revealed that M-LSTM outperforms other test methods in developing the long-term prediction method. HIGHLIGHTS A highly accurate long-term inflow forecasting framework is developed using meteorological data based on long short-term memory neural network.; The meteorological dataset of the study area is obtained using inverse distance weighting.; The maximal information coefficient can adequately measure the degree of correlation between meteorological data and inflow.; The proposed framework demonstrates superior forecast performance.;

The uneven velocity distribution formed at the lateral inlet/outlet poses a significant risk of damaging the trash racks. Reasonable design of the inlet/outlet structures requires the consideration of two major aspects: the average velocity (Vm) and the coefficient of unevenness (Uc). This paper developed an optimization framework that combines an interpretable Gradient Boosting Decision Tree (SOBOL-GBDT) with a Non-dominated Sorting Genetic Algorithm (NSGA-II). 125 conditions are simulated by performing CFD simulations to generate the dataset, followed by GBDT implemented to establish a nonlinear mapping between the input parameters including vertical (α) and horizontal (β) diffusion angles, diffusion segment length (LD), channel area (CA), and the objectives Uc and Vm. The SOBOL analysis reveals that in Uc prediction, CA and α play more significant roles in the model development compared to β and LD. Besides, GBDT is observed to better capture interactive effects of the input parameters compared with other machine learning models. Subsequently, a multi-objective optimization framework using GBDT-NSGA-II is developed. The framework calculates the optimal Pareto front and determines the best solution using a pseudo-weight method. The results demonstrate that this framework leads to significant improvements in flow separation reduction in the diffusion segment and the normalized velocity distribution. The SOBOL-GBDT-NSGA-II framework facilitates a rational and effective design of the inlet/outlet. HIGHLIGHTS Sobol indices can effectively explain the effect of individual and their interactions on the predictive model.; Within multi-objective optimization problems, the GBDT-NSGA-II framework can effectively identify optimal solutions.; In the optimal shape, lateral inlet/outlet reduces the flow separation areas in the channel, simultaneously diminishing the likelihood of flow-induced vibrations in the trash racks.;

To solve the problems of localization and identification of fish in the complex fishway environment, improving the accuracy of fish detection, this paper proposes an object detection algorithm YOLORG, and a fishway fish detection dataset (FFDD). The FFDD contains 4,591 images from the web and lab shots and labeled with the LabelIMG tool, covering fish in a wide range of complex scenarios. The YOLORG algorithm, based on YOLOv8, improves the traditional FPN–PAN network into a C2f Multi-scale feature fusion network with a Gather-and-Distribute mechanism, which solves the problem of information loss accompanied by the network in the fusion of feature maps of different sizes. Also, we propose a C2D Structural Re-parameterization module with a rich gradient flow and good performance to further improve the detection accuracy of the algorithm. The experimental results show that the YOLORG algorithm improves the mAP50 and mAP50-95 by 1.2 and 1.8% compared to the original network under the joint VOC dataset, and also performs very well in terms of accuracy compared to other state-of-the-art object detection algorithms, and is able to detect fish in very turbid environments after training on the FFDD. HIGHLIGHTS We propose an FFDD fish detection dataset.; We propose a Structural Re-parameterization convolution module C2D.; We propose a C2f Multi-scale feature fusion network to solve the problem of information loss in the YOLOv8 network.; We propose a YOLORG-series model constructed by C2D Structural Re-parameterization module and C2f Multi-scale feature fusion network.; The proposed method has fewer parameters.;

Sediment accumulation in the sewer is a source of cascading problems if left unattended and untreated, causing pipe failures, blockages, flooding, or odour problems. Good maintenance scheduling reduces dangerous incidents, but it also has financial and human costs. In this paper, we propose a predictive model to support the management of maintenance routines and reduce cost expenditure. The solution is based on an architecture composed of an autoencoder and a feedforward neural network that classifies the future sediment deposition. The autoencoder serves as a feature reduction component that receives the physical properties of a sewer section and reduces them into a smaller number of variables, which compress the most important information, reducing data uncertainty. Afterwards, the feedforward neural network receives this compressed information together with rain and maintenance data, using all of them to classify the sediment deposition in four thresholds: more than 5, 10, 15, and 20% sediment deposition. We use the architecture to train four different classification models, with the best score from the 5% threshold, being 82% accuracy, 70% precision, 76% specificity, and 88% sensitivity. By combining the classifications obtained with the four models, the solution delivers a final indicator that categorizes the deposited sediment into clearly defined ranges. HIGHLIGHTS A predictive model based on an autoencoder and a feedforward neural network is proposed to predict future sediment deposition.; The model's best score achieves an 82% accuracy rate for the 5% sediment deposition threshold, ensuring the identification of sediment deposition in the sewer system.; The proposed model provides an easier-to-interpret indicator for water utilities to adapt their process accordingly.;

To address the issue of low accuracy and inefficiency in the traditional parameter calibration methods for the SWMM model, this paper constructs an automatic parameter calibration model based on multi-objective optimisation algorithms. Firstly, the Sobol method and GLUE method are utilised to determine sensitive parameters and their ranges, aiming to narrow down the solution space and expedite the model-solving speed. Secondly, the NSGA-3 multi-objective optimisation algorithm based on the Pareto theory is applied for the optimisation and calibration of sensitive parameter sets. The model is validated in the rainwater drainage system with independent runoff in a residential area in a northwestern city in China. The results show that parameters such as N-Imperv and KSlope are highly sensitive to the model output under the land-use conditions of the study area. The simulation accuracy of the multi-objective continuous optimisation algorithm is significantly better than that of the single-objective genetic algorithm. The simulation results of the SWMM model under multi-objective optimisation demonstrate a certain level of reliability and stability. The research findings can provide technical support for the automatic calibration of SWMM model parameters, accurate model simulation, and application. HIGHLIGHTS The study provides a quantitative interpretation of the results of sensitivity parameter screening and ranking for the SWMM model.; During the construction of the objective function, the SWMM model system's multi-objective continuous optimisation criteria were adopted.; Combining the concepts of uncertainty and optimisation.;

Most of the existing water quality models for water distribution networks assume complete mixing at junctions. Albeit few models offer the possibility to consider incomplete mixing (IM) at junctions, most of them were developed under laboratory conditions and for equal pipe size junctions. In real-world distribution networks, however, cross junctions of 150 × 100 × 150 × 100 mm or 100 × 150 × 150 × 150 mm are common, yet no model has been developed for these configurations. This paper presents a new equation to compute concentrations in cross junction outlets while considering IM for six cross junction configurations, including unequal pipe sizes and 150 mm pipes. For each cross junction configuration, mixing was studied under 25 flow scenarios in the laboratory and 40 simulated flow scenarios using OpenFOAM software. Two new flow rate ratios were selected as independent variables to compute different outlet concentrations. For two specific cross junctions with equal pipe sizes, the root-mean-squared error between the observed and simulated concentrations of the newly developed model was 0.02, while it was 0.05 and 0.07, respectively, for the AZRED IM model and the Shao et al. IM model. HIGHLIGHTS Laboratory experiments were conducted under real-world conditions with 150 mm pipes.; New principal combinations of independent variables for mixing were identified.; A single equation was developed for mixing in different cross junction configurations.; The developed model reproduces observations more accurately than the other models.;

Approximate Bayesian computation (ABC) relaxes the need to derive explicit likelihood functions required by formal Bayesian analysis. However, the high computational cost of evaluating models limits the application of Bayesian inference in hydrological modeling. In this paper, multivariate adaptive regression splines (MARS) are used to expedite the ABC calibration process. The MARS model is trained using 6,561 runoff simulations generated by the soil and water assessment tool (SWAT) model and subsequently replaces the SWAT model to calculate the objective functions in ABC and multi-objective evolutionary algorithm (MOEA). In experiments, MARS can successfully reproduce the runoff time series simulations of the SWAT model at a low time cost, with a runoff variance determination coefficient of 0.90 as compared to the Monte Carlo method. MARS-assisted ABC can quickly and accurately estimate the parameter distributions of the SWAT model. The comparison of ABC with non-Bayesian MOEAs helps in the selection of an appropriate calibration approach. HIGHLIGHTS Approximate Bayesian computation (ABC) was used to calibrate the complex hydrological model.; Multivariate adaptive regression splines (MARS) were used to expedite the ABC process.; The ability of MARS-assisted ABC for calibration of the SWAT model was demonstrated.; The calibration results of ABC and multi-objective evolutionary algorithms were compared.; MARS-assisted ABC has the potential to extend the use of ABC in hydrological modeling.;

An accurate rainfall–runoff observation is critical for giving a warning of a potential damage early enough to allow appropriate response to the disaster. The long short-term memory (LSTM)-based rainfall–runoff model has been proven to be effective in runoff prediction. Previous research has typically utilized multiple information sources as the LSTM training data. However, when there are many sequences of input data, the LSTM cannot get nonlinear valid information between consecutive data. In this paper, a novel informer neural network using empirical wavelet transform (EWT) was first proposed to predict the runoff based only on the single rainfall data. The use of EWT reduced the non-linearity and non-stationarity of runoff data, which increased the accuracy of prediction results. In addition, the model introduced the Fractal theory to divide the rainfall and runoff into three parts, by which the interference caused by excessive data fluctuations could be eliminated. Using 15-year precipitation from the GPM satellite and runoff from the USGS, the model performance was tested. The results show that the EWT_Informer model outperforms the LSTM-based models for runoff prediction. The PCC and training time in EWT_Informer were 0.937, 0.868, and 1 min 3.56 s, respectively, while those provided by the LSTM-based model were 0.854, 0.731, and 4 min 25.9 s, respectively. HIGHLIGHTS The informer network was innovatively introduced into rainfall–runoff prediction, which reduced time and spatial complexity.; The empirical wavelet transform was utilized to enhance the treatment of non-linearity and non-stationarity.; Fractal theory was applied to eliminate the interference caused by excessive data fluctuations.;

Power generation from fossil fuels has long had a negative impact on the environment. Nowadays, a paradigm shift in power generation is being witnessed, with increasing investment in renewable energy sources. Despite this progress, efficient energy storage is still limited. Given this challenge, pumped storage technology can be one of the viable solutions. This involves storing gravitational energy by pumping water into a reservoir at a higher altitude, which is later converted into electrical energy using a turbine. This paper studies a pump hydro storage system (PHS) operation in water supply systems (WSSs), with the aim of minimizing operating costs and evaluating its effectiveness. Replacing conventional pumps with pump-as-turbines (PATs) provides a flexible and cost-effective approach. The proposed methodology aims to optimize the operation of these PATs considering dynamic energy prices. The developed computational model was applied to different operational scenarios and analyzed in terms of cost-effectiveness. The results show that the lower the average ratio between time-differentiated purchase and fixed sell energy tariffs, the greater the optimization potential of using PAT. In the WSS case study analyzed, energy cost reductions of 43.4–68.1% were achieved, demonstrating the effectiveness of PHS in WSS particularly for energy tariffs with large variations. HIGHLIGHTS The management of a pumped storage system integrated into water supply systems is still little explored.; The integration of dynamic energy pricing with PAT operation represents an opportunity for water utilities to reduce their costs.; The computational model developed using optimization algorithms allows for efficient operation of PAT in WSSs.;

Modeling of pressure-dependent users’ consumption is mandatory to simulate accurately the hydraulics of water distribution networks (WDNs). Several software solutions already exist for this purpose, but none of them actually permits the easy integration and testing of new physical processes. In this paper, we propose a new Python simulator that implements a state-of-the-art pressure-dependent model (PDM) of users’ consumptions based on the Wagner’s pressure–outflow relationship (POR). We tested our simulator on eight large and complex WDNs, for different levels of users’ demands. The results show similar precision and efficiency to the ones obtained by the authors of the original model with their MATLAB implementation. Moreover, in case of fully satisfied users’ demands, our simulator provides the same results as EPANET 2.0 in comparable computational times. Finally, our simulator is integrated into the open-source, collaborative, multi-platform, and Git versioned Python framework OOPNET (Object-Oriented Python framework for water distribution NETworks analyses); thus, it can be easily reused and/or extended by a large community of WDN modelers. All this work represents a preliminary step before the incorporation of new processes such as valves, pumps, and pressure-dependent background leakage outflows. HIGHLIGHTS A new simulator of pressure-dependent consumption in water distribution network.; Coded in Python, based on a state-of-the-art MATLAB model, easy to extend.; Numerical experiments on networks composed of up to 19,647 pipes and 17,986 nodes.; Instabilities handled through regularization and line search along the Newton descent.; Preliminary step for integration of new processes and first contribution to OOPNET.;

Accurate runoff prediction is vital in efficiently managing water resources. In this paper, a hybrid prediction model combining complete ensemble empirical mode decomposition with adaptive noise, variational mode decomposition, CABES, and long short-term memory network (CEEMDAN-VMD-CABES-LSTM) is proposed. Firstly, CEEMDAN is used to decompose the original data, and the high-frequency component is decomposed using VMD. Then, each component is input into the LSTM optimized by CABES for prediction. Finally, the results of individual component predictions are combined and reconstructed to produce the monthly runoff predictions. The hybrid model is employed to predict the monthly runoff at the Xiajiang hydrological station and the Yingluoxia hydrological station. A comprehensive comparison is conducted with other models including back propagation (BP), LSTM, etc. The assessment of each model's prediction performance uses four evaluation indexes. Results reveal that the CEEMDAN-VMD-CABES-LSTM model showcased the highest forecast accuracy among all the models evaluated. Compared with the single LSTM, the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the Xiajiang hydrological station decreased by 71.09 and 65.26%, respectively, and the RMSE and MAPE of the Yingluoxia hydrological station decreased by 65.13 and 40.42%, respectively. The R and NSEC of both sites are near 1. HIGHLIGHTS A novel model (CEEMDAN-VMD-CABES-LSTM) is proposed for monthly runoff prediction.; CEEMDAN-VMD effectively reduces the complexity of the original monthly runoff series.; CABES enhances the generalization ability and prediction performance of the LSTM.; The four evaluation indicators and seven benchmark models are employed to verify the superiority of the developed model.;

In this paper, the discharge coefficient prediction model for this structure in a subcritical flow regime is first established by extreme learning machine (ELM) and Bayesian network, and the model's performance is analyzed and verified in detail. In addition, the global sensitivity analysis method is introduced to the optimal prediction model to analyze the sensitivity for the dimensionless parameters affecting the discharge coefficient. The results show that the Bayesian extreme learning machine (BELM) can effectively predict the discharge coefficients of the symmetric stepped labyrinth side weir. The range of 95% confidence interval [−0.055,0.040] is also significantly smaller than that of the ELM ([−0.089,0.076]) and the Kernel extreme learning machine (KELM) ([−0.091,0.081]) at the testing stage. The dimensionless parameter ratio of upstream water depth of stepped labyrinth side weir p/y1 has the greatest effect on the discharge coefficient Cd, accounting for 55.57 and 54.17% under single action and other parameter interactions, respectively. Dimensionless step number bs/L has little effect on Cd, which can be ignored. Meanwhile, when the number of steps is less (N = 4) and the internal head angle is smaller (θ = 45°), a larger discharge coefficient value can be obtained. HIGHLIGHTS The discharge coefficient model of a symmetrical stepped labyrinth side weir is developed.; The quantitative model of the discharge coefficient is established.; The discharge characteristics of a symmetrical stepped labyrinth side weir are analyzed comprehensively.; The effects of different dimensionless parameters on the discharge coefficient are compared.;

Flooding is one of the most frequent natural hazards and causes more economic loss than all the other natural hazards. Fast and accurate flood prediction has significance in preserving lives, minimizing economic damage, and reducing public health risks. However, current methods cannot achieve speed and accuracy simultaneously. Numerical methods can provide high-fidelity results, but they are time-consuming, particularly when pursuing high accuracy. Conversely, neural networks can provide results in a matter of seconds, but they have shown low accuracy in flood map generation by all existing methods. This work combines the strengths of numerical methods and neural networks and builds a framework that can quickly and accurately model the high-fidelity flood inundation map with detailed water depth information. In this paper, we employ the U-Net and generative adversarial network (GAN) models to recover the lost physics and information from ultra-fast, low-resolution numerical simulations, ultimately presenting high-resolution, high-fidelity flood maps as the end results. In this study, both the U-Net and GAN models have proven their ability to reduce the computation time for generating high-fidelity results, reducing it from 7–8 h down to 1 min. Furthermore, the accuracy of both models is notably high. HIGHLIGHTS In our study area, our models have demonstrated the capability to dramatically decrease the computation time required to generate high-fidelity results, reducing it from 7–8 h to 1 min.; The GAN model displays a lower sensitivity to changes in input resolution compared with the U-Net model.; The proposed method effectively recovers lost information because of the large grid size in the low-resolution geometry.;

This research introduces a novel nonlinear Muskingum model for river flood routing, aiming to enhance accuracy in modeling. It integrates lateral inflows using the Whale Optimization Algorithm (WOA) and employs a distributed Muskingum model, dividing river reaches into smaller intervals for precise calculations. The primary goal is to minimize the Sum of Square Errors (SSE) between the observed and modeled outflows. Our methodology is applied to six distinct flood hydrographs, revealing its versatility and efficacy. For Lawler's and Dinavar's flood data, the single-reach Muskingum model outperforms multi-reach versions, demonstrating its effectiveness in handling lateral inflows. For Lawler's data, the single-reach model (NR = 1) yields optimal parameters of K = 0.392, x = 0.027, m = 1.511, and β = 0.010, delivering superior results. Conversely, when fitting flood data from Wilson, Wye, Linsley, and Viessman and Lewis, the multi-reach Muskingum model exhibits better overall performance. Remarkably, the model excels with the Viessman and Lewis flood data, especially with two reaches (NR = 2), achieving a 21.6% SSE improvement while employing the same parameter set. This research represents a significant advancement in flood modeling, offering heightened accuracy and adaptability in river flood routing. HIGHLIGHTS Flooding can have a significant impact on people and environment.; Distributed Muskingum model was introduced to improve the accuracy and efficiency of the model's calculations.; This study suggests WOA to improve the local optimum.; Considering lateral flow is the major feature developed for the new approach in our paper.; The results will be used in Red River of the North to anticipate floods in ND and MN in the US.;

Leakages in water distribution networks (WDNs) affect the hydraulic state of the entire or a large part of the network. Statistical correlation computed among pressure sensors monitoring network nodes aids the detection and localization of such leaks. This opens the possibility to work with water network databases, where graph signal processing (GSP) tools aid in understanding changes in pressure signals due to leakages in the hydraulic system. This paper presents a methodology for time-varying pressure signals on graph structures. The core of this methodology is based on changing of pressure, due to leaks, that modifies the graph structure. Computing for each time step a new topology of the graph and applying centrality analysis based on PageRank, it is possible to identify the presence of new leaks at the water system. A confusion matrix evaluates the precision of the proposed methodology on defining where and when such leakages start and end. Seven leaks are used to validate the process, which presented 86% in accuracy terms. The results show the benefits of the method in terms of speed, computational efficiency, and precision in detecting leakages. HIGHLIGHTS A novel data analysis methodology for leak detection in water distribution networks.; Correlation of pressure data for the creation of temporal graphs.; Analysis of water networks as graphs and vertices ranking by the PageRank metric.;

In this paper, an advanced three-dimensional (3D) computational fluid dynamics (CFD) model is used to analyse the steady-state hydrodynamics of laminar flow through an extended partial blockage (PB) in a pressurised pipe. PB corresponds to one of the main faults affecting pipelines. In fact, it reduces its carrying capacity with economic consequences, and as it does not give rise to any external evidence, its detection can be very challenging. The performance of the model is evaluated by comparing the numerical results with the available experimental data from the literature. Subsequently, the velocity and pressure distributions are analysed, and the main features of the flow field are described in terms of both local and global dimensionless parameters. Furthermore, the behaviour of the discharge coefficient is also investigated. The obtained results confirm that steady-state measurements can identify the presence of PB and follow its evolution over time but cannot detect its location and size. On the other hand, the location and severity of PBs can be provided by means of transient tests. HIGHLIGHTS Analysis of the steady-state laminar flow through an extended partial blockage.; Validation of a 3D CFD model of an extended partial blockage with experimental data from the literature.; Velocity and pressure fields characterisation around an extended partial blockage.; Characterisation and evaluation in time of an extended partial blockage.; Hydrodynamics of a flow through an extended partial blockage.;

Monitoring of sewer networks (SNs) is an important task whose planning can be related to various purposes, for example contaminant detection and epidemiological studies. This paper proposes two different approaches for the identification of a monitoring system in SNs. The first one proposes the identification of the best monitoring points starting from the knowledge of the hydraulic behavior of the system with respect to specific sensor threshold values through an optimization procedure that maximizes the reliability in detecting a contaminant. A new mathematical model is developed and a global optimization solver is employed to perform the optimization procedure. The second approach is based on the complex network theory (CNT) tools, adopting the in-relevance-based harmonic centrality, and does not require any hydraulic simulation. The metric is evaluated for each node of the network and provides a range of nodes, classified with respect to their importance, useful to identify suitable locations for sensors. With reference to both a benchmark and a real SN, the comparison between the results achieved by both strategies indicates that the two approaches provide comparable solutions in terms of sensor location. HIGHLIGHTS Comparison between two different approaches for monitoring planning in sewer networks.; Use of a global optimization solver to find the optimal location of sensors maximizing the network reliability.; Use of the in-based harmonic centrality as a tool to identify suitable locations for sensors referring to only topological information.; Despite being based on different concepts, the two approaches provide comparable results.;

The temporal and spatial resolutions of rainfall data directly affect the accuracy of hydrological simulation. Weather radar has been used in business in China, but the uncertainty of data is large. At present, research on radar data and fusion in small and medium-sized basins in China is very weak. In this paper, taking the Duanzhuang watershed as an example, based on station data, Shijiazhuang's radar data are preprocessed, optimized and fused. Eleven rainfall events are selected for fusion by three methods and quality evaluation, and three flood simulations are used to test their effect. The results show that preprocessing and initial optimization have poor effects on radar data improvement. The rainfall proportional coefficient fusion method performs best in rainfall spatial estimation, where the R2 values of the three inspection stations are increased to 0.51, 0.78 and 0.82. Three fusion datasets in the peak flow and flood volume of flood simulation perform better than station data. For example, in the No.20210721 flood, the NSE of the three fusion data increased by 39, 30 and 48%. This shows that the fusion method can effectively improve the data accuracy of radar and can obtain high temporal and spatial resolution rainfall data. HIGHLIGHTS Preprocessing and initially optimizing Shijiazhuang's S-band weather radar.; Using three fusion methods to optimize radar rainfall data and station data.; Evaluating and comparing fusion data on the different scales and by the HEC-HMS model simulation.;

To achieve an integrated river basin management for the Cagne catchment (France) and better predict floods, various modelling tools are integrated within a unified framework, forming a decision support system (DSS). In the paper, an integrated modeling approach employing deterministic distributed hydrological (MIKE SHE), hydraulic (MIKE 21 FM), and hydrogeological (FEFLOW) models is presented. The hydrological model was validated with recorded data and following a sensitivity analysis for optimizing grid resolution with 20 m. The hydraulic model based on MIKE 21 FM utilizes the results generated by the MIKE SHE model as boundary conditions, producing inundation maps for both normal and extreme periods. The hydrogeological model addresses the various complex relationships taking place within the catchment and was validated with piezometer data. The integration of these three models into a DSS provides a valuable tool for decision-makers to manage the Cagne catchment and the water-related issues more effectively during various hydrological situations. This comprehensive modelling framework underscores the importance of interdisciplinary approaches for addressing complex hydrological processes and contributes to improved flood management strategies in the catchment. HIGHLIGHTS This study combines hydrological, hydraulic, and hydrogeological modeling techniques to do integrated water management and flood forecasting.; This study concentrates on a small-scale catchment, employing high-resolution data to achieve a more detailed and comprehensive analysis.; This study integrates an underground canal into urban flood management, offering a more precise depiction of urban flood dynamics.;

This paper introduces a multi-objective optimisation approach for the challenging problem of efficient sensor placement in water distribution networks for contamination detection. An important question is how to identify the minimal number of required sensors without losing the capacity to monitor the system as a whole. In this study, we adapted the NSGA-II multi-objective optimisation method by applying centrality mutation. The approach, with two objectives, namely the minimisation of Expected Time of Detection and maximisation of Detection Network Coverage (which computes the number of detected water contamination events), is tested on a moderate-sized benchmark problem (129 nodes). The resulting Pareto front shows that detection network coverage can improve dramatically by deploying only a few sensors (e.g. increase from one sensor to three sensors). However, after reaching a certain number of sensors (e.g. 20 sensors), the effectiveness of further increasing the number of sensors is not apparent. Further, the results confirm that 40–45 sensors (i.e. 31 − 35% of the total number of nodes) will be sufficient for fully monitoring the benchmark network, i.e. for detection of any contaminant intrusion event no matter where it appears in the network. HIGHLIGHTS It is possible to significantly reduce the number of undetected events by deploying only a few more sensors.; Placing sensors on 31−35% of nodes is sufficient for full monitoring of the case study network.; Maximising the opportunity to detect events prioritises the selection of nodes that have neither the highest centrality nor the lowest.; Minimising the detection time of events prioritises nodes with centrality at/close to the extremes.;

Coupling models of different dimensions is one of the most important yet under-represented challenges. This paper introduces a new modeling strategy to streamline a more flexible and effective integrated one-dimensional (1D)/two-dimensional (2D) model for floodplains along lowland rivers. The 1D model, utilizing the finite volume method, solves the Saint–Venant equations, while the 2D mesh employs unstructured quadrilateral elements. The two strategies couple the 1D/2D models: direct 1D/2D connection by the law of mass conservation at supernode, and lateral 1D/2D model connection by spillways at riverbank. The coupling strategy in F28 guarantees the water balance and the conservation of momentum at the integrated 1D/2D nodes. The model was applied to the Mekong Delta to address the capacity of hydrodynamic simulations integrating various water infrastructures. Results showed that the developed model has a strong potential to be applied to other lowland rivers worldwide with complex infrastructures. HIGHLIGHTS Innovative 1D–2D model coupling enhances floodplain flexibility.; Integrated nodes ensure water balance and momentum conservation.; Unstructured 2D mesh improves river discharge simulation.; Model employs explicit finite volume, semi-analytical coupling, and parallel approach for stability and precision.;

The discharge estimation in rivers is crucial in implementing flood management techniques and essential flood defence and drainage systems. During the normal flood season, water flows solely in the main channel. During a flood, rivers comprise a main channel and floodplains, collectively called a compound channel. Computing the discharge is challenging in non-prismatic compound channels where the floodplains converge or diverge in a longitudinal direction. Various soft computing techniques have nowadays become popular in the field of water resource engineering to solve these complex problems. This paper uses a hybrid soft computing technique – artificial neural network and particle swarm optimization (ANN–PSO) and multivariate adaptive regression splines (MARS) to model the discharge in non-prismatic compound open channels. The analysis considers nine non-dimensional parameters – bed slope, relative flow depth, relative longitudinal distance, hydraulic radius ratio, angle of convergence or divergence, flow aspect ratio, relative friction factor, and area ratio – as influencing factors. A gamma test is carried out to determine the optimal combination of input variables. The developed MARS model has produced satisfactory results, with a mean absolute percentage error (MAPE) of less than 7% and an R2 value of more than 0.90. HIGHLIGHTS Using traditional methods to estimate discharge in non-prismatic compound channels provides unsatisfactory results.; Discharge is estimated in non-prismatic compound channels using two soft computing techniques ANN–PSO and MARS.; Influencing parameters for the prediction of discharge are identified using the Gamma test.; Different model performances have been carried out for different ranges of width ratio and relative flow depth.;

Sewage systems are essential for the efficient functioning of cities. Wastewater contains solids and organic matter that pose health risks, making it necessary to optimize the sewage system design. In recent years, optimization tools have been introduced to minimize costs while still complying with regulations. Despite this, traditional designs still dominate, but the use of optimization methodologies can significantly reduce construction costs. For this reason, this research will compare the construction costs of a sewage system designed optimally and one designed using traditional methods, to determine the cost difference between the two. In a sector of Bogota, Colombia, a sewerage system was already built and designed according to Colombian laws using traditional methodologies. The information from this area was used to implement the UTOPIA program, created by the Universidad de los Andes. This program uses the Shortest Path Problem with the Bellman-Ford algorithm to design the network and minimize costs. The results show that the optimized system was about 15% cheaper than the traditional one, and it ensured that all pipelines met the design restrictions. Optimized sewage systems are a useful alternative for ensuring universal access to safe drinking water, increasing sewerage coverage, and reducing problems associated with inadequate design. HIGHLIGHTS The paper makes a comparison between a traditional design methodology and an optimization one, for large sewers.; It proves that an optimized design not only obtains lower costs but for all pipes complies with the hydraulic restrictions.; The methodology introduced will help sewer coverage in developing countries.; It will help in reaching the SDG No. 6.; It is an interesting application of operational research.;

Water reservoirs are essential to ensure water supply to both the population and agriculture, especially in the Mediterranean basin. In some cases, analyses of water intended for human consumption have detected high levels of agrochemicals. Knowing the possible origin of these products is complex because there may be many agricultural plots within the reservoir basin. In this paper, we introduce a methodology to obtain the set of agricultural plots whose rainwater reaches the reservoir and in what proportion they affect the points where chemical analyses are performed. The method implements an extension of the D8 algorithm for the calculation of the drainage network, in which additional information about the land-use type of the area, as well as rainfall maps, are also considered. In order to facilitate the user's analysis of the data, a plugin has been implemented in QGIS. This allows usability and easy interaction with the visual information. The Rumblar reservoir basin, located in Andalusia (Spain) has been studied as a use case, surrounded by olive orchards. The result is a replicable methodology for any other water reservoir and for carrying out an individualized study of agricultural plots. HIGHLIGHTS Water reaching a reservoir is of great environmental interest.; Farms that contribute the most water to the reservoir can be found out.; Modification of the D8 algorithm allows adding new functionality to the drainage network creation process.; The methodology allows for the addition of real information such as land use type and rainfall maps.; The end-users can use an interactive tool implemented as QGIS plugin.;

Numerous studies on the pressure fluctuations and cavity volume variations of a transient cavitation flow in viscoelastic pipes are available in the literature. However, the effect of water temperature on the cavity volume and energy conversion has been studied less often. This paper employs the discrete vapor cavity model using quasi-steady friction and quasi-two-dimensional friction models to calculate the cavity volume for different water temperatures and investigates the effects of water temperature on the appearance of the first cavitation at the downstream valve, and on the pressure damping in a tank-piping-valve system using an integrated energy analysis approach. The results show that the differences between the pressure and energy variations of the transient cavitation flow simulated using different models were minimal under different water temperature conditions. Moreover, as the water temperature increased, the appearance time of the cavity is postponed, and the volume of the cavity decreases. The energy dissipation increases continuously with an increase in the volume of the cavitation and water temperature in viscoelastic pipes. This study provides valuable insights into the variation pattern of the cavity and the effect of vapor cavities on the rise and decay of the pipeline pressure in different situations. HIGHLIGHTS This study investigated the effect of different water temperatures on the calculated results of transient cavitation flow in viscoelastic pipes.; The energy variations during transient flow and transient cavitation flow in viscoelastic pipes were compared.; The concept of the conversion ratio from kinetic energy to elastic energy was proposed, where a smaller conversion ratio may result in a larger energy dissipation.;

This paper presents a new framework for modeling the bargaining process among stakeholders by coupling social choice and bargaining methods. Based on this framework, two methods of evolutionary bargaining coupled with Borda count (BBC) and evolutionary bargaining coupled with pairwise voting (BPV) are proposed, and the results of applying them to resolve the challenging problems of allocating water and reclaimed wastewater in agricultural regions are analyzed. After proposing some candidate scenarios of allocating water and reclaimed wastewater, non-dominated scenarios are determined. Then, in the first level of bargaining, using a social choice technique, each stakeholder chooses the most desirable scenario out of the non-dominated ones, regardless of the utilities of other stakeholders. The selected scenarios by all stakeholders can provide them an estimate of other stakeholders' expected utilities. This enables each stakeholder in the next step of bargaining to suggest a scenario that improves their own utility, while providing a minimum acceptable utility of other stakeholders. If the bargaining results in more than one scenario, a social choice method is applied to find the most preferred scenario. The applicability and performance of the proposed framework are evaluated by applying it to the Varamin plain, in the south-east of Tehran, Iran. HIGHLIGHTS A new framework for modeling the bargaining process is presented.; The proposed method can explicitly simulate the iterative process of real-world bargaining problems.; Social choice and bargaining methods are coupled in one framework.; The methodology is applied in a challenging real-world problem.; The results provide policies for allocating water and reclaimed wastewater in agricultural regions that are acceptable to stakeholders.;

Water security and urban flooding have become major sustainability issues. This paper presents a novel method to introduce rates of change as the state-of-the-art approach in artificial intelligence model development for sustainability agenda. Multi-layer perceptron (MLP) and deep learning long short-term memory (LSTM) models were considered for flood forecasting. Historical rainfall data from 2008 to 2021 at 11 telemetry stations were obtained to predict flow at the confluence between Klang River and Ampang River. The initial results of MLP yielded poor performance beneath normal expectations, which was R = 0.4465, MAE = 3.7135, NSE = 0.1994 and RMSE = 8.8556. Meanwhile, the LSTM model generated a 45% improvement in its R-value up to 0.9055. Detailed investigations found that the redundancy of data input that yielded multiple target values had distorted the model performance. Qt was introduced into input parameters to solve this issue, while Qt+0.5 was the target value. A significant improvement in the results was detected with R = 0.9359, MAE = 0.7722, NSE = 0.8756 and RMSE = 3.4911. When the rates of change were employed, an impressive improvement was seen for the plot of actual vs. forecasted flow. Findings showed that the rates of change could reduce forecast errors and were helpful as an additional layer of early flood detection. HIGHLIGHTS A highly accurate flood forecasting system based on deep learning has been developed.; Multi-lead ahead forecasting for streamflow has been investigated.; The novel architecture model has been successfully applied for controlling streamflow in SMART tunnel.; The proposed new model architecture could be applied to forecast river streamflow in different hydrological areas;

Hydraulic transient analysis allows the condition assessment of pipeline systems by the measurement of a system's transient pressure response subject to input pressure excitations. The detection of a pressure wave's arrival time and amplitude at one or more sections can be used to detect unexpected anomalies, such as leaks, blockages, or corroded sections. Wave separation approaches, based on signal processing techniques involving two sensors, enable a directional attribution to any measured pressure perturbations. Being able to determine the direction of origin of a perturbation through a signal-splitting approach greatly facilitates anomaly detection through the resolution of this ambiguity. The signal-splitting procedure can be sensitive to the analysis conditions (i.e. the signal processing procedure used, the presence of noise within the signal, and the spacing of the sensors) and, as a result, produce spurious results. This paper explores this issue and proposes, and analyses, a range of strategies to improve the signal-splitting results. The strategies explored involve the consideration of alternative time- and frequency-domain formulations; the use of filters and wavelet to condition the signal; and processing the time-shifted differenced signal as opposed to the original raw signal. Results are presented for a range of numerical and laboratory systems. HIGHLIGHTS The pressure signals acquired at two measurement sections can be separated, pointing out positive and negative traveling wave components.; The separation procedure can be helpful in the transient-based diagnosis in complex systems.; Different techniques are developed in time and frequency domains and tested on numerical and laboratory experiments.; Wavelet transform of separated signals is also derived.;

Ice condition monitoring (ICM) is critical for the operation and maintenance of water supply infrastructure in cold regions. Existing approaches either depend on ground-level sensors or satellite photography for ICM, which suffer from high maintenance costs or inadequate precision. Computer vision (CV) has the potential to tackle the limitations by providing a precise and scalable solution based on near-shore cameras and increasingly affordable drones. To explore the potential of CV for ICM, this paper presents a systematic study of salient image features for differentiating typical ice evolvement phases throughout the freeze–thaw cycle. First, ice condition during the freeze–thaw cycle is studied to provide a categoric system of typical ice stages. Second, multiple image feature descriptors are proposed to characterize the distinction between different ice conditions. Finally, with the proposed descriptors as input, two support vector machines (SVMs) are trained to classify the ice condition for automatic ICM. Experiments have been implemented to identify salient features for ice characterization. It was found that the SVMs can achieve 71.9 and 77.3% accuracy for the prediction of ice stage and ice flow strength, respectively. Future research is suggested to develop the research findings into practical solutions for webcams or drone-based automatic ICM. HIGHLIGHTS Computer vision is used to monitor ice conditions in water supply infrastructure.; Image features are handcrafted to characterize different ice conditions.; Effectiveness of the features is quantified and evaluated by correlation analysis.;

Flow-regulated stormwater ponds providing safe outflow discharges prevail as the primary stormwater management tool for stream protections. Detailed pond geometries are essential metrics in pond monitoring technologies, which convert the point-based water level measurements to areal/volumetric ponding water estimations. Unlike labour-intensive surveys (e.g., RTK-GNSS or total stations), UAV-photogrammetry and airborne-LiDAR have been advocated as cost-effective alternatives to acquire high-quality datasets. In this paper, we compare the use of these two approaches for stormwater pond surveys. With reference to RTK-GNSS in-situ observations, we identify their geometric and hydraulic discrepancies based on six stormwater ponds from three aspects: (i) DEMs, (ii) stage-curves and (iii) outflow discharges. Three main findings are outlined: (i) for wet ponds where moisture environments are dominant, UAV-photogrammetry outperforms (infrared) airborne-LiDAR, where airborne-LiDAR yields 0.15–0.54 NSEoutflow, which is unacceptable; (ii) for dry ponds, UAV-photogrammetry obtains 0.88–0.89 NSEoutflow as poor vegetation penetrations; two correction methods (i.e., grass removal and shifted stage-curves) are proposed, indicating good alignment to RTK-GNSS observations and (iii) UAV-photogrammetry delivers

In this paper, a deterioration model is created and used to simulate the life cycle of a water distribution network (WDN). Then, two strategies – leakage fixing and pipe cleaning – are evaluated to rehabilitate its capacity to attend the demand. In order to implement the deterioration model, the following parameters were considered: growth of the consumer population, increase in leakage rate, functional pipe deterioration and reduction of the hydraulic capacity of the pumps. For the leakage fixing, a fixed reduction rate in water losses was considered throughout the entire WDN until a minimum reference value was reached. For pipe rehabilitation, leaning was considered at a rate of 1% of the total length of the network per year. In each of the rehabilitation strategies, a cost–benefit analysis was carried out using the net present value. The results showed that both alternatives can restore the capacity of the WDN, with the pipe cleaning presenting a better economic impact. HIGHLIGHTS This study presents a methodology to understand the impact that the deterioration of a water distribution network (WDN) has on its operation.; This study brings an estimate of the time when a WDN should be rehabilitated as it deteriorates throughout its useful life.; This study presents two ways to rehabilitate a WDN and the economic impact of each of these implemented measures.;