Showing papers in "Journal of Hydroinformatics in 2017"
TL;DR: In this paper, the authors extended the TFR-based leak detection method to relatively more complex pipeline connection situations and derived leak-induced patterns of transient responses analytically using the transfer matrix method for systems with different pipe junctions, which thereafter are used for the analysis of pipe leakage conditions in the system.
Abstract: The transient frequency response (TFR) method has been widely developed and applied in the literature to identify and detect potential defects such as leakage and blockage in water supply pipe systems. This type of method was found to be efficient, economic and non-intrusive for pipeline condition assessment and diagnosis, but its applications so far are mainly limited to single and simple pipeline systems. This paper aims to extend the TFR-based leak detection method to relatively more complex pipeline connection situations. The branched and looped pipe junctions are firstly investigated for their influences to the system TFR, so that their effects can be characterized and separated from the effect of other components and potential leakage defects in the system. The leak-induced patterns of transient responses are derived analytically using the transfer matrix method for systems with different pipe junctions, which thereafter are used for the analysis of pipe leakage conditions in the system. The developed method is validated through different numerical experiments in this study. Based on the analytical analysis and numerical results, the applicability and accuracy as well as the limitations of the developed TFR-based leak detection method are discussed for practical applications in the paper.
74 citations
TL;DR: In this article, a self-adaptive evolutionary extreme learning machine (SAELM) was used to develop a new model for the prediction of local scour around bridge piers using 476 field pier scour measurements with four shapes of piers.
Abstract: Accurate prediction of pier scour can lead to economic design of bridge piers and prevent catastrophic incidents. This paper presents the application of self-adaptive evolutionary extreme learning machine (SAELM) to develop a new model for the prediction of local scour around bridge piers using 476 field pier scour measurements with four shapes of piers: sharp, round, cylindrical, and square. The model network parameters are optimized using the differential evolution algorithm. The best SAELM model calculates the scour depth as a function of pier dimensions and the sediment mean diameter. The developed SAELM model had the lowest error indicators when compared to regression-based prediction models for RMSE (0.15, 0.65, respectively) and MARE (0.50, 2.0, respectively). The SAELM model was found to perform better than artificial neural networks or support vector machines on the same dataset. Parametric analysis showed that the new model predictions are influenced by pier dimensions and bed-sediment size and produce similar trends of variations of scour-hole depth as reported in literature and previous experimental measurements. The prediction uncertainty of the developed SAELM model is quantified and compared with existing regression-based models and found to be the least, ±0.03 compared with ±0.10 for other models.
58 citations
TL;DR: The chaotic PSO-SVM model is applied to predict the daily groundwater levels in Huayuan landslide and the weekly, monthly groundwater level in Baijiabao landslide in the Three Gorges Reservoir Area in China and shows that there are chaos characteristics in the groundwater levels.
Abstract: Many nonlinear models have been proposed to forecast groundwater level. However, the evidence of chaos in groundwater levels in landslide has not been explored. In addition, linear correlation analyses are used to determine the input and output variables for the nonlinear models. Linear correlation analyses are unable to capture the nonlinear relationships between the input and output variables. This paper proposes to use chaos theory to select the input and output variables for nonlinear models. The nonlinear model is constructed based on support vector machine (SVM). The parameters of SVM are obtained by particle swarm optimization (PSO). The proposed PSO-SVM model based on chaos theory (chaotic PSO-SVM) is applied to predict the daily groundwater levels in Huayuan landslide and the weekly, monthly groundwater levels in Baijiabao landslide in the Three Gorges Reservoir Area in China. The results show that there are chaos characteristics in the groundwater levels. The linear correlation analysis based PSO-SVM (linear PSO-SVM) and chaos theory-based back-propagation neural network (chaotic BPNN) are also applied for the purpose of comparison. The results show that the chaotic PSO-SVM model has higher prediction accuracy than the linear PSO-SVM and chaotic BPNN models for the test data considered.
58 citations
TL;DR: In this article, the authors presented the first attempt to link the multi-algorithm genetically adaptive search method (AMALGAM) proposed by Vrugt and Robinson (2007) with a groundwater model to define pumping rates within a well distributed set of Pareto solutions.
Abstract: This study presents the first attempt to link the multi-algorithm genetically adaptive search method (AMALGAM) proposed by Vrugt and Robinson (2007) with a groundwater model to define pumping rates within a well distributed set of Pareto solutions. The pumping rates along with three minimization objectives, i.e. minimizing shortage affected by the failure to supply, modified shortage index and minimization of extent of drawdown within prespecified regions, were chosen to define an optimal solution for groundwater drawdown and subsidence. Hydraulic conductivity, specific yield parameters of a modular three-dimensional finite-difference (MODFLOW) groundwater model were first optimized using cuckoo optimization algorithm by minimizing the sum of absolute deviation between the observed and simulated water table depths. These parameters were then applied in AMALGAM to optimize the pumping rate variables for an arid groundwater system in Iran. The Pareto parameter sets yielded satisfactory results when maximum and minimum drawdowns of the aquifer were defined in a range of −40 to +40 cm/year. Overall, ‘Modelling – Optimization – Simulation’ procedure was capable to compute a set of optimal solutions displayed on a Pareto front. The proposed optimal solution provides sustainable groundwater management alternatives to decision makers in arid region.
47 citations
TL;DR: The adaptive neuro-fuzzy inference system (ANFIS) is applied to predict axial velocity and flow depth in a 90° sharp bend and results indicate that ANFIS-GP-Hybrid predicts velocity best followed by flow depth.
Abstract: Investigating flow patterns in sharp bends is more essential than in mild bends due to the complex behaviour exhibited by sharp bends. Flow variable prediction in bends is among several concerns of hydraulics scientists. In this study, the adaptive neuro-fuzzy inference system (ANFIS) is applied to predict axial velocity and flow depth in a 90° sharp bend. The experimental velocity and flow depth data for five discharge rates of 5, 7.8, 13.6, 19.1 and 25.3 L/s are used for training and testing the models. In ANFIS training, the two algorithms employed are back propagation (BP) and a hybrid of BP and least squares. In model design, the grid partitioning (GP) and sub-clustering methods are used for fuzzy inference system generation. The results indicate that ANFIS-GP-Hybrid predicts velocity best followed by flow depth.
44 citations
TL;DR: In this article, the authors compared three overland flow models, a full dynamic model (shallow water equation), a local inertial equations model (gravity wave model), and a diffusive wave model (parallel Diffusive Wave model), coupled with the same full dynamic sewer network model.
Abstract: In this study we compared three overland flow models, a full dynamic model (shallow water equation), a local inertial equations model (gravity wave model), and a diffusive wave model (parallel diffusive wave model). The three models are coupled with the same full dynamic sewer network model (SIPSON). We adopted the volume exchange between sewer and overland flow models, and the hydraulic head and discharge rates at the linked manholes to evaluate differences between the models. For that purpose we developed a novel methodology based on RGB scale. The test results of a real case study show a close agreement between coupled models in terms of the extents of flooding, depth and volume exchanged, despite highly complex flows and geometries. The diffusive wave model gives slightly higher maximum flood depths and a slower propagation of the flood front when compared to the other two models. The local inertial model shows to a slight extent higher depths downstream as the wave front is slower than the one in the fully dynamic model. Overall, the simplified overland models can produce comparable results to fully dynamic models with less computational cost.
37 citations
TL;DR: In this article, a mathematical simulation-optimization model is proposed for optimal management of aquifers using a mathematical optimization model which relies on the stability of water quality and quantity, considering salinity.
Abstract: This study addresses the issue of optimal management of aquifers using a mathematical simulation- optimization model which relies on the stability of water quality and quantity, considering salinity. In this research first we developed a hydrological model (SWAT) to estimate recharge rates and its spatiotemporal distribution. Then, groundwater simulation of the basin was simulated and calibrated using MODFLOW 2000 and water quality was simulated and calibrated using MT3DMS. Afterwards, a multi-objective optimization model (MOPSO) and embed simulation models as tools to assess the objective function was carried out in order to produce a simulation-optimization model. Finally, a sustainability index to assess Pareto front9s answers and three management scenarios (continuing previous operation, 30% increasing and reduction in previous operation) was developed. The results show that the majority of Pareto optimal answers have more sustainability index than a 30% reduction of operation with the best answer of 0.059. Relatively, the sustainability index of 30% reduction of operation is 0.05.
32 citations
TL;DR: In this article, the accuracy of quadratic approximations for the Hazen-Williams (HW) head loss formula has been analyzed for water supply networks, and two smooth polynomial approximation domains were proposed to minimize the absolute and relative errors from the original non-smooth HW head loss function.
Abstract: This paper presents a novel analysis of the accuracy of quadratic approximations for the Hazen–Williams (HW) head loss formula, which enables the control of constraint violations in optimisation problems for water supply networks. The two smooth polynomial approximations considered here minimise the absolute and relative errors, respectively, from the original non-smooth HW head loss function over a range of flows. Since quadratic approximations are used to formulate head loss constraints for different optimisation problems, we are interested in quantifying and controlling their absolute errors, which affect the degree of constraint violations of feasible candidate solutions. We derive new exact analytical formulae for the absolute errors as a function of the approximation domain, pipe roughness and relative error tolerance. We investigate the efficacy of the proposed quadratic approximations in mathematical optimisation problems for advanced pressure control in an operational water supply network. We propose a strategy on how to choose the approximation domain for each pipe such that the optimisation results are sufficiently close to the exact hydraulically feasible solution space. By using simulations with multiple parameters, the approximation errors are shown to be consistent with our analytical predictions.
26 citations
TL;DR: In this paper, a new model is developed for regional drought frequency analysis, which utilizes the L-moments approach and adjusted charged system search as an advanced meta-heuristic algorithm, in which some modifications on the equations of the algorithm are performed to improve its standard variant.
Abstract: The parameter estimation of statistical distributions is important for regional frequency analysis (RFA). The accuracy of different parts of RFA such as estimating the regional quantiles of the selected statistical distribution, determining the heterogeneity measure, and choosing the best distribution based on the Monte Carlo simulation, may be influenced by using the different values of regional parameters. To fulfill this aim, in the present study, a new model is developed for regional drought frequency analysis. This model utilizes the L-moments approach and the adjusted charged system search as an advanced meta-heuristic algorithm, in which some modifications on the equations of the algorithm are performed to improve its standard variant. The verification of the regional parameters estimated by the new methodology yields accurate results compared to other models. Furthermore, this study illustrates the usefulness of the robust discordancy measure against the classic one. For this purpose, different values of the subset factors (α) are utilized in the robust discordancy measure, and finally, the best value of subset factor is found equal to 0.8, which can accurately recognize discordant sites within the region.
25 citations
TL;DR: In this article, the results of a large number of tests carried out at the Water Engineering Laboratory of the University of Perugia, Italy, have been used to characterize a standard type of pressure reducing valve in steady-state conditions and in unsteady state conditions to check its transient response.
Abstract: A pressure reducing valve (PRV) regulates the outlet pressure regardless of the fluctuating flow and varying inlet pressure, thereby reducing leakage and mitigating the stress on the downstream water distribution network (WDN). Notwithstanding the crucial importance of PRVs, few experimental data are available in the literature. The aim of this paper is to overcome this gap by means of the results of a large number of tests carried out at the Water Engineering Laboratory of the University of Perugia, Italy. These tests have been executed on a standard type of PRV in steady-state conditions, to characterize it, and in unsteady-state conditions, to check its transient response. A broad range of laboratory conditions simulating possible events in WDNs has been examined and both short and long duration monitoring have been carried out. The analysis of the tests demonstrates the versatility of PRVs as a powerful tool for pressure management, and also when the flow condition changes according to the users9 demand pattern. In fact, their transient response is appropriate with small pressure oscillations generated by the PRV self-adjustment. Moreover, proper PRV modelling has to include both its mechanical behaviour and the characteristics of the pressure pipe system in which it is installed.
25 citations
TL;DR: In this article, the authors proposed to use a bank of models instead of a single model for forecasting one particular hour, each model is designed for forecasting a particular hour and the architecture design and the training process are performed using genetic algorithms.
Abstract: Efficient management of a drinking water network reduces the economical costs related to the water production and transport (pumping). Model predictive control (MPC) is nowadays a quite well-accepted approach for the efficient management of the water networks because it allows formulating the control problem in terms of the optimization of the economical costs. Therefore, short-term forecasts are a key issue in the performance of MPC applied to water distribution networks. However, the short-term horizon demand forecast in a horizon of 24 hours in an hourly based scale presents some challenges as the water consumption can change from one day to another, according to certain patterns of behavior (e.g., holidays and business days). This paper focuses on the problem of forecasting water demand for the next 24 hours. In this work, we propose to use a bank of models instead of a single model. Each model is designed for forecasting one particular hour. Hourly models use artificial neural networks. The architecture design and the training process are performed using genetic algorithms. The proposed approach is assessed using demand data from the Barcelona water network.
TL;DR: Results show that the relatively uniform precipitation–runoff relationship has changed since 1993 in the upstream reaches and since 1970 in the middle and downstream reaches, and human activities have become the dominant influencing factor on runoff variation since the 1970s.
Abstract: Runoff in the Yellow River (YR) of China is steadily declining due to climate change and human activities. In this study, the basic trend and abrupt changes of precipitation at 63 meteorological stations and runoff as measured at six hydrological stations from 1956 to 2010 are analyzed. Results indicate that 38 stations exhibit negative precipitation trends. These stations are mainly located in the lower reaches. All six hydrological stations exhibit declining runoff trends. Abrupt runoff changes were mainly noted in the downstream portion of the basin. These variations then expanded to the middle and upper reaches. A precipitation–runoff double cumulative curve was used to detect the breakpoint of the precipitation–runoff relationship and to identify the impacts of human activities on runoff in the YR. Results show that the relatively uniform precipitation–runoff relationship has changed since 1993 in the upstream reaches and since 1970 in the middle and downstream reaches. Additionally, the relationship was more sensitive in the Lanzhou section. Human activities have become the dominant influencing factor on runoff variation since the 1970s. After the 1990s, the percentages of runoff variations due to human activities were 74.87%, 82.2%, 80.63%, and 88.71% at the Lanzhou, Todaoguai, Huayuankou, and Lijin stations, respectively.
TL;DR: This study investigates the application of robust data-mining methods including genetic programming and artificial neural networks for predicting the maximum scour depth at seawalls under the broken and breaking waves action and indicates that both the GP and ANNs models functioned significantly better than the existing empirical formulas.
Abstract: Accurate prediction of maximum scour depth is important for the optimum design of seawall structure. Owing to the complex interaction of the incident waves, sediment bed, and seawalls, the prediction of the scour depth is not an easy task to accomplish. Undermining the recent experimental and numerical advancement, the available empirical equations have limited accuracy and applicability. The aim of this study is to investigate the application of robust data-mining methods including genetic programming (GP) and artificial neural networks (ANNs) for predicting the maximum scour depth at seawalls under the broken and breaking waves action. The performance of GP and ANNs models has been compared with the existing empirical formulas employing statistical measures. The results indicated that both the GP and ANNs models functioned significantly better than the existing empirical formulas. Furthermore, the capability of GP was used to produce meaningful mathematical rules, and an analytical formula for predicting the maximum scour depth at seawalls under breaking and broken waves' attacks was developed by utilizing GP.
TL;DR: In this paper, the shutter opening degree (SD), valve hydraulic resistance (RES) and valve head loss (HL) were used to control the shutter closing degree of a pressure control valve in a water distribution network.
Abstract: In water distribution networks (WDNs) the classic pressure control valves (PCVs) are mechanical/hydraulic devices aimed at maintaining the target pressure just downstream or upstream the PCV pipe, namely pressure reduction or sustaining valves. From a modelling standpoint, the major drawback of such local control is that classic PCVs may require target pressure varying over time with the pattern of delivered water because the controlled node is not strategic for the optimal WDN pressure control. Current information and communication technology allows transferring streams of pressure data from any WDN node to the PCV. Thus, remotely real-time control (RRTC) permits real-time electric regulation of PCVs to maintain a fixed target pressure value in strategic critical nodes, resulting in optimal control of pressure and background leakages. This paper shows three strategies for the electric regulation of RRTC PCVs, which use as control variables the shutter opening degree (SD), the valve hydraulic resistance (RES) and the valve head loss (HL). The Apulian network is used to compare the three strategies, while the application on the real Oppegard WDN yields further discussions. Results show that HL and RES strategies outperform SD; constraining the maximum shutter displacement helps SD stability although it still needs calibration.
TL;DR: In this paper, the authors used an Acoustic Doppler Velocimeter and optical fiber probe to measure the instantaneous velocities and air entrainment of plunge pools from overflow nappe flows.
Abstract: When the capacity of the spillway of a dam is exceeded for a given flood, overtopping occurs; in such cases potentially dangerous hydrodynamic actions and scour downstream of the dam need to be foreseen. Detailed studies of jets impinging in plunge pools from overflow nappe flows are scarce. This work addresses plunge pool flows, and compares numerical results against our own experiments. The energy dissipation is larger than 75% of the impingement jet energy. Instantaneous velocities and air entrainment were obtained with the use of an Acoustic Doppler Velocimeter and optical fibre probe, respectively. Mean velocity field and turbulence kinetic energy profiles were determined. To identify the level of reliability of models, numerical simulations were carried out by using the ‘homogeneous’ model of ANSYS CFX, together with different turbulence closures. The numerical results fall fairly close to the values measured in the laboratory, and with expressions for submerged hydraulic jumps and horizontal wall jets. The observations can be well predicted for characterized profiles at a minimum distance of 0.40 m downstream from the stagnation point, horizontal velocities greater than 40% of the maximum velocity in each profile, and when the ratio of the water cushion depth to the jet thickness is lower than 20.
TL;DR: The symbiotic organisms search (SOS) evolutionary algorithm is introduced to the optimization of reservoir operation and proved superior to the GA and the WCA in optimizing the objective functions of the two reservoir systems.
Abstract: This work introduces the symbiotic organisms search (SOS) evolutionary algorithm to the optimization of reservoir operation. Unlike the genetic algorithm (GA) and the water cycle algorithm (WCA) the SOS does not require specification of algorithmic parameters. The solution effectiveness of the GA, SOS, and WCA was assessed with a single-reservoir and a multi-reservoir optimization problem. The SOS proved superior to the GA and the WCA in optimizing the objective functions of the two reservoir systems. In the single reservoir problem, with global optimum value of 1.213, the SOS, GA, and WCA determined 1.240, 1.535, and 1.262 as the optimal solutions, respectively. The superiority of SOS was also verified in a hypothetical four-reservoir optimization problem. In this case, the GA, WCA, and SOS in their best performance among 10 solution runs converged to 97.46%, 99.56%, and 99.86% of the global optimal solution. Besides its better performance in approximating optima, the SOS avoided premature convergence and produced lower standard deviation about optima.
TL;DR: In this article, the authors presented a new automatic approach for simplification of 1D hydraulic networks using trimming and merging techniques, with performance evaluated in a 1D/2D case study.
Abstract: Evaluation of pluvial flood risk is often based on computations using 1D/2D urban flood models. However, guidelines on choice of model complexity are missing, especially for 1D network models. This study presents a new automatic approach for simplification of 1D hydraulic networks (SAHM) using trimming and merging techniques, with performance evaluated in a 1D/2D case study. Decreasing the number of elements in the 1D model by 66% yielded a 35% decrease in computation time of the coupled 1D/2D simulation. The simplifications increased flow in some downstream branches and removing nodes eliminated connection to some areas. This promoted errors in 2D flood results with changes in spatial location of flooding in the reduced 1D/2D models. Applying delayed rain inputs to compensate for changes in travel time and preserving network volume by expanding node diameters did not improve overall results. Investigations on the expected annual damages ( EAD ) showed that differences in EAD are smaller than deviations in the simulated flooded areas, suggesting that spatial changes are limited to local displacements. Probably, minor improvements of the simplification procedure will further improve results of the reduced models.
TL;DR: In this article, a modified Harmony Search Multi-Objective optimization algorithm is developed to solve the pump scheduling problem in WDNs and the hydraulic solver EPANET 2.0 is coupled with the algorithm to assess the feasibility of the achieved solutions.
Abstract: Pumps are installed in Water Distribution Networks (WDNs) to ensure adequate service levels in the case of poor water pressure (e.g. because of low elevation of reservoirs or high head losses within the WDN). In such cases optimal pump scheduling is often required for the opportunity of significant energy saving. Optimizing the pump operation also allows a reduction in damages and maintenance times. Among the approaches available in the literature to solve the problem, meta-heuristic algorithms ensure reduced computational times, although they are not able to guarantee the optimal solution can be found. In this paper, a modified Harmony Search Multi-Objective optimization algorithm is developed to solve the pump scheduling problem in WDNs. The hydraulic solver EPANET 2.0 is coupled with the algorithm to assess the feasibility of the achieved solutions. Hydraulic constraints are introduced and penalties are set in case of violation of the set constraints to reduce the space of feasible solutions. Results show the high performances of the proposed approach for pumping optimization, guaranteeing optimal (or near optimal) solutions with short computational times.
TL;DR: Genetic algorithm (GA) optimization methods are used to achieve the desired WDN segmentation conditions in terms of reducing the operating pressure, thus reducing the system9s real losses; and reducing the water age, thus improving the feeling of water freshness and preventing growth of disinfection byproducts.
Abstract: Dividing a water distribution network (WDN) into district metered areas (DMAs) is the first vital step towards pressure management and real losses reduction. However, other factors of water quality such as the water age must be taken into account while forming DMAs. The current study uses genetic algorithm (GA) optimization methods to achieve the desired WDN segmentation conditions in terms of: (a) reducing the operating pressure, thus reducing the system9s real losses; and (b) reducing the water age, thus improving the feeling of water freshness and preventing growth of disinfection byproducts. Techniques based on GA are a proven way to provide a very good solution to optimization problems. The solution is obtained using an objective function and setting boundary constraints. The formation of the objective functions is tested through Matlab9s optimization toolbox. The logic of the objective functions9 formulation for both the operating pressure and the water age optimization is recorded and analyzed. The method9s application utilized a sample network model assisted by EPANET and Bentley9s WaterGEMS software tools. The morphology of the DMAs is presented for each scenario, as well as the results of the network9s segmentation according to the operating pressure and the water age.
TL;DR: Wang et al. as mentioned in this paper assessed and classified shallow groundwater quality with improved Nemerow pollution index, multi-layer perceptron artificial neural network (MLP-ANN) optimized with a back-propagation algorithm and wavelet neural networks (WNN) methods in a coastal aquifer, Fujian Province, South China.
Abstract: Shallow groundwater is generally of great interest to the community due to its easy availability. However, it is very sensitive to external stimulus. In this paper, shallow groundwater quality is assessed and classified with improved Nemerow pollution index, multi-layer perceptron artificial neural network (MLP-ANN) optimized with a back-propagation algorithm and wavelet neural network (WNN) methods in a coastal aquifer, Fujian Province, South China. The data used in three models were collected during the pre-monsoon over the period 2004–2011. The eight parameters, total dissolved solids, total hardness, chemical oxygen demand, chloride, sulphate, nitrate, nitrite and fluorides, were selected to characterize groundwater quality classification based on the National Quality Standard for Groundwater (GB/T 14848-93). The results of MLP-ANN and WNN are interpreted by mean absolute error, root mean square error and R 2 (determination coefficient) criteria. The results obtained from three methods demonstrate that WNN has a higher accuracy compared with the other two methods. The study reveals that these methods are efficient tools for assessing groundwater quality.
TL;DR: In this article, a meshless local Petrov-Galerkin (MLPG) is used to simulate groundwater flow in Birjand unconfined aquifer located in Iran in a transient state for 1 year with a monthly time step.
Abstract: In recent decades, due to reduction in precipitation, groundwater resource management has become one of the most important issues considered to prevent loss of water. Many solutions are concerned with the investigation of groundwater flow behavior. In this regard, development of meshless methods for solving the groundwater flow system equations in both complex and simple aquifers9 geometry make them useful tools for such investigations. The independency of these methods to meshing and remeshing, as well as its capability in both reducing the computation requirement and presenting accurate results, make them receive more attention than other numerical methods. In this study, meshless local Petrov–Galerkin (MLPG) is used to simulate groundwater flow in Birjand unconfined aquifer located in Iran in a transient state for 1 year with a monthly time step. Moving least squares and cubic spline are employed as approximation and weight functions respectively and the simulated head from MLPG is compared to the observation results and finite difference solutions. The results clearly reveal the capability and accuracy of MLPG in groundwater simulation as the acquired root mean square error is 0.757. Also, with using this method there is no need to change the geometry of aquifer in order to construct shape function.
Abstract: Groundwater monitoring plays a significant role in groundwater management. This study presents an optimization method for designing groundwater-level monitoring networks. The proposed design method was used in the Eshtehard aquifer, in central Iran. Three scenarios were considered to optimize the locations of the observation wells: 1) designing new monitoring networks, 2) redesigning existing monitoring networks, and 3) expanding existing monitoring networks. The kriging method was utilized to determine groundwater levels at non-monitoring locations for preparing the design data base. The optimization of the groundwater monitoring network had the objectives of 1) minimizing the root mean square error and 2) minimizing the number of wells. The non-dominated sorting genetic algorithm (NSGA-II) was applied to optimize the network. Inverse distance weighting interpolation was used in NSGA-II to estimate the groundwater levels while optimizing network design. Results of the study indicate that the proposed method successfully optimizes the design of groundwater monitoring networks that achieve accuracy and cost-effectiveness.
TL;DR: In this paper, the authors consider the problem of estimating the shape of the membership function of flows in the branches of a water supply and a looped water distribution system, based on the extension principle of the fuzzy sets and a new operation of fuzzy subtraction.
Abstract: Conventionally, the design of urban water supply and distribution systems is based on the assumption that all the involved parameters are known a priori and remain unaltered throughout the life cycle of the system. However, significant uncertainties do appear during the analysis and design of these systems, such as the equivalent pipe roughness and the actual internal diameters of the pipes. To study these uncertainties, the water supply and the looped water distribution systems are studied separately. For the water supply system, these uncertainties are incorporated in the analysis of the system, using the extension principle of the fuzzy sets and a new operation of the fuzzy subtraction. Based on the calculation of head losses for each branch of the system, the nodal heads are obtained as fuzzy numbers. In regard to the looped water distribution system, a methodology is developed and proposed, based on the extension principle and leading to several optimisation problems with respect to the branches of the system. The aim of the proposed methodology is to determine the α -cuts and finally produce the shape of the membership function of flows in the branches of the system. Both methodologies are illustrated by numerical examples.
TL;DR: In this paper, an optimal design of a water distribution network is proposed for large irrigation networks, which is built upon an existing optimization method (NSGA-II), but the authors are proposing its effective application in a new two-step optimization process.
Abstract: In this work, an optimal design of a water distribution network is proposed for large irrigation networks. The proposed approach is built upon an existing optimization method (NSGA-II), but the authors are proposing its effective application in a new two-step optimization process. The aim of the paper is to demonstrate that not only is the choice of method important for obtaining good optimization results, but also how that method is applied. The proposed methodology utilizes as its most important feature the ensemble approach, in which more optimization runs cooperate and are used together. The authors assume that the main problem in finding the optimal solution for a water distribution optimization problem is the very large size of the search space in which the optimal solution should be found. In the proposed method, a reduction of the search space is suggested, so the final solution is thus easier to find and offers greater guarantees of accuracy (closeness to the global optimum). The method has been successfully tested on a large benchmark irrigation network.
TL;DR: In this article, an approach for the control of a pumping plant feeding a tank at the inlet of a water distribution system is presented, aimed at minimizing the energy costs by maximizing pumping during off-peak electricity tariff periods.
Abstract: An approach for the control of a pumping plant feeding a tank at the inlet of a water distribution system is presented. The approach is aimed at minimizing the energy costs by maximizing pumping during off-peak electricity tariff periods. It is based on trigger levels which are variable during the day according to a prefixed pattern in order to ensure that the water level in the elevated tank is at its minimum and maximum values at the end of the peak and off-peak tariff periods, respectively. The pattern of the trigger levels is defined by solving a multi-objective problem aimed at minimizing the energy costs and the number of pump switches. The approach was applied to a couple of real cases with a single tank. The approach was compared with other methodologies typically used for pump control, i.e. fixed trigger levels (FTLs) and pump scheduling (PS). The results show for the two particular cases that the proposed approach achieves energy costs that are lower than those obtainable by using FTLs, and comparable with those obtainable by using PS. This is based on achieving a similar number of pump switches.
TL;DR: In this article, an algorithm is developed in C++ language to achieve desired segmentation conditions in terms of operating pressure reduction, thus reducing the system9s real water losses and residual chlorine concentration reduction thus preventing disinfection byproducts9 growth.
Abstract: Dividing a water distribution network (WDN) in the optimal district metered areas (DMAs) formation is one task that usually troubles water utility managers. The present paper utilizes optimization methods to achieve desired segmentation conditions in terms of (a) operating pressure reduction, thus reducing the system9s real water losses and (b) residual chlorine concentration reduction thus preventing disinfection byproducts9 growth. Exploiting the numerous possibilities offered by the inter-connection of Matlab and EPANET software tools, an algorithm is developed in C++ language. The algorithm reads all significant data of a WDN as an output of EPANET. The first algorithm calculates the optimal allocation of a given number of closed isolation valves in terms of water losses9 reduction, considering restrictions for network9s proper operation. The second algorithm calculates the optimal formation of DMAs in terms of water quality improvement. Both algorithms can be applied in any WDN. The outcome is the optimal set of closed pipes that leads to the optimal formation of DMAs in a given network. The closing of pipes (by installing isolation valves) determines the optimal formation of DMAs. The basic concept of both algorithms and their application in a case study network9s hydraulic model are presented.
TL;DR: The MODFA solves multi-objective multi-reservoir operation system with the purpose of hydropower generation that are highly nonlinear that classical methods cannot solve.
Abstract: Classical methods have severe limitations (such as being trapped in local optima, and the curse of dimensionality) to solve optimization problems. Evolutionary or meta-heuristic algorithms are currently favored as the tools of choice for tackling such complex non-linear reservoir operations. This paper evaluates the performance of an extended multi-objective developed firefly algorithm (MODFA). The MODFA script code was developed using the MATLAB programming language and was applied in MATLAB to optimize hydropower generation by a three-reservoir system in Iran. The two objectives used in the present study are the maximization of the reliability of hydropower generation and the minimization of the vulnerability to generation defficits of the three-reservoir system. Optimal Paretos (OPs) obtained with the MODFA are compared with those obtained with the multi-objective genetic algorithm (MOGA) and the multi-objective firefly algorithm (MOFA) for different levels of performance thresholds (50%, 75%, and 100%). The case study results demonstrate that the MODFA is superior to the MOGA and MOFA for calculating proper OPs with distinct solutions and a wide distribution of solutions. This study9s results show that the MODFA solves multi-objective multi-reservoir operation system with the purpose of hydropower generation that are highly nonlinear that classical methods cannot solve.
TL;DR: The obtained results show that the PSO-ARIMA outperforms the ANN and STS approaches since it can optimize simultaneously integer and real parameters and provides better accuracy in terms of MAPE, coefficient of determination, and average absolute relative error.
Abstract: In this paper, a modeling-identification approach for the Monthly Municipal Water Demand system in Hail region, Saudi Arabia, is developed. This approach is based on an auto-regressive integrated moving average (ARIMA) model tuned by the particle swarm optimization (PSO). The ARIMA (p, d, q) modeling requires estimation of the integer orders p and q of the AR and MA parts; and the real coefficients of the model. More than being simple, easy to implement and effective, the PSO-ARIMA model does not require data pre-processing (original time-series normalization for artificial neural network (ANN) or data stationarization for traditional stochastic time-series (STS)). Moreover, its performance indicators such as the mean absolute percentage error (MAPE), coefficient of determination ( R 2 ), root mean squared error (RMSE) and average absolute relative error (AARE) are compared with those of ANN and STS. The obtained results show that the PSO-ARIMA outperforms the ANN and STS approaches since it can optimize simultaneously integer and real parameters and provides better accuracy in terms of MAPE (5.2832%), R 2 (0.9375), RMSE (2.2111 × 10 5 m 3 ) and AARE (5.2911%). The PSO-ARIMA model has been implemented using 69 records (for both training and testing). The results can help local water decision makers to better manage the current water resources and to plan extensions in response to the increasing need.
TL;DR: A wave separation algorithm, accounting for transmission dynamics, is presented, which enables the extraction of directional travelling waves by using two closely placed pressure sensors at one measurement site (referred as a dual-sensor).
Abstract: Over the past two decades, techniques have been developed for pipeline leak detection and condition assessment using hydraulic transient waves (i.e. water hammer waves). A common measurement strategy for applications involves analysis of signals from a single pressure sensor located at each measurement site. The measured pressure trace from a single sensor is a superposition of reflections coming from upstream, and downstream, of the sensor. This superposition brings complexities for signal processing applications for fault detection analysis. This paper presents a wave separation algorithm, accounting for transmission dynamics, which enables the extraction of directional travelling waves by using two closely placed pressure sensors at one measurement site (referred as a dual-sensor). Two typical transient incident pressure waves, a pulse wave and a step wave, are investigated in numerical simulations and laboratory experiments. Comparison of the wave separation results with their predicted counterparts shows the wave separation algorithm is successful. The results also show that the proposed wave separation technique facilitates transient-based pipeline condition assessment.
TL;DR: In this paper, the authors presented a model simulating the behaviour of urban water distribution networks under normal operating conditions, as well as during a period of intermittent water supply (IWS) operations.
Abstract: Water authorities in countries facing water shortage problems are implementing intermittent water supply (IWS) policies, as a measure to conserve and control their national water resources. Implementation of such measures affects the behaviour of the water pipe systems during the operation stage. The research work presented herein presents a model simulating the behaviour of urban water distribution networks (WDNs) under normal operating conditions, as well as during a period of IWS operations. The modelling and analysis, based on an eight-year dataset (2003–2010) from a local Water Board, takes into account information related to breakage incidents within the WDN as well as external factors to perform vulnerability assessment of the pipe network. The results of the performed survival and cluster analysis show that during the implementation period of IWS operations, and right after that period, there is a significant increase in the deterioration rate of the affected network. Further, there is a change in the comparative importance of the factors affecting the network condition and their contribution to the WDN vulnerability.