Showing papers in "Journal of Hydroinformatics in 2012"

Comparison of multivariate adaptive regression splines with coupled wavelet transform artificial neural networks for runoff forecasting in Himalayan micro-watersheds with limited data

Abstract: Himalayan watersheds are characterized by mountainous topography and a lack of available data. Due to the complexity of rainfall–runoff relationships in mountainous watersheds and the lack of hydrological data in many of these watersheds, process-based models have limited applicability for runoff forecasting in these areas. In light of this, accurate forecasting methods that do not necessitate extensive data sets are required for runoff forecasting in mountainous watersheds. In this study, multivariate adaptive regression spline (MARS), wavelet transform artificial neural network (WA-ANN), and regular artificial neural network (ANN) models were developed and compared for runoff forecasting applications in the mountainous watershed of Sainji in the Himalayas, an area with limited data for runoff forecasting. To develop and test the models, three micro-watersheds were gauged in the Sainji watershed in Uttaranchal State in India and data were recorded from July 1 2001 to June 30 2003. It was determined that the best WA-ANN and MARS models were comparable in terms of forecasting accuracy, with both providing very accurate runoff forecasts compared to the best ANN model. The results indicate that the WA-ANN and MARS methods are promising new methods of short-term runoff forecasting in mountainous watersheds with limited data, and warrant additional study.

A real options approach to the design and architecture of water supply systems using innovative water technologies under uncertainty

Abstract: Water supply has become a priority for developed and developing nations of the world. Conventional water resources alone cannot meet the growing demand for water in urban cities. Management of the problem is amplified by uncertainty associated with different development strategies. Singapore has limited conventional water resources and progressively architects its water supply system through acquiring and sustaining multiple (alternative) water resources through innovative technologies. The full rationale and merits of such a policy cannot be properly understood based on traditional project valuation methods alone. This paper provides decision support using a real options approach by evaluating innovative water technologies from multiple perspectives under uncertainty. This paper demonstrates that incorporating innovative water technologies into water supply systems can concurrently improve water supply from the financial, political and socioeconomic perspectives. The development of innovative water technologies provides flexibility to the water supply system, and is a fundamental and effective means of risk management. The evaluation of innovative water technologies is based on an integrated real options approach, which provides decision support for architecting water supply systems under uncertainty. The approach gives specific tangible values for the water technologies and complements the general prescriptive Integrated Water Resources Management (IWRM) framework.

Resilience and entropy as indices of robustness of water distribution networks

Abstract: The use of entropy and resilience indices for measuring robustness of water distribution networks has been investigated. The effects on network performance, caused by the failure of one or two links, have been evaluated by means of several indices for two existing medium sized water distribution networks serving two towns in southern Italy. All the possible network configurations obtained by suppressing one or two links have been studied, excluding only the cases in which disconnection of some nodes from the remaining part of the network occurred. The hydraulic simulations, carried out with a demand-driven approach by means of the EPANET2 software, have shown that, unlike entropy, resilience may represent a useful index of network robustness with regard to link failures.

Leakage fault detection in district metered areas of water distribution systems

Abstract: Fault tolerance and security in drinking water distribution operations are important issues that have received increased attention in the last few years. In this work the problem of leakage detection is formulated within a systems engineering framework, and a solution methodology to detect leakages in a class of distribution systems is proposed. Specifically, the case where water utilities use standard flow sensors to monitor the water inflow in a District Metered Area (DMA) is considered. The goal is to design algorithms which analyze the discrete inflow signal and determine as early as possible whether a leakage has occurred in the system. The inflow signal is normalized to remove yearly seasonal effects, and a leakage fault detection algorithm is presented, which is based on learning the unknown, time-varying, weekly periodic DMA inflow dynamics using an adaptive approximation methodology for updating the coefficients of a Fourier series; for detection logic the Cumulative Sum (CUSUM) algorithm is utilized. For reference and comparison, a second solution methodology based on night-flow analysis using the normalized inflow signal is presented. To illustrate the solution methodology, results are presented based on randomized simulated leakages and real hydraulic data measured at a DMA in Limassol, Cyprus.

Comparative analysis of overland flow models using finite volume schemes

Abstract: In this paper attention is first focused on a comparative analysis of three hydraulic models for overland flow simulations. In particular, the overland flow was considered as a 2D unsteady flow and was mathematically described using three approaches (fully dynamic, diffusive and kinematic waves). Numerical results highlighted that the differences among the simulations were not very important when the simulations referred to commonly used ideal tests found in the literature in which the topography is reduced to plane surface. Significant differences were observed in more complicated tests for which only the fully dynamic model was able to provide a good prediction of the observed discharges and water depths. Then, attention is focused on the fully dynamic model and in particular on the analysis of two numerical schemes (TVD-MacCormack and HLL) and the influence of the grid size. Numerical tests carried out on irregular topography show that, as the grid size decreases, the performance of the HLL scheme becomes closer to that of the TVD-MacCormack scheme in shorter computational times at least for high rainfall intensity.

GSM-enabled remote monitoring of rural handpumps: a proof-of-concept study

Abstract: The continued expansion of mobile network coverage in rural Africa provides an opportunity for simple and low-cost hydroinformatic innovations to measure and transmit data on handpump use for policy and management improvements. We design, build and test a Waterpoint Data Transmitter to determine its robustness, functionality and scalability. Results demonstrate that this novel application using simple microprocessor, accelerometer and global system for mobile communications (GSM) components has significant potential in recording graduated time-step information flows of lever pumps which can be modelled into a reasonable water volume use approximation. Given the systemic informational deficit for rural waterpoints in Africa, where one in three handpumps is likely to be non-functioning, this innovation has the potential to provide universal, low-cost and immediate data to guide timely maintenance responses and planning decisions, as well as drive greater accountability and transparency in donor and government behaviour.

Calibration of Xinanjiang model parameters using hybrid genetic algorithm based fuzzy optimal model

Abstract: Conceptual rainfall–runoff (CRR) model calibration is a global optimization problem with the main objective to find a set of optimal model parameter values that attain a best fit between observed and simulated flow. In this paper, a novel hybrid genetic algorithm (GA), which combines chaos and simulated annealing (SA) method, is proposed to exploit their advantages in a collaborative manner. It takes advantage of the ergodic and stochastic properties of chaotic variables, the global search capability of GA and the local optimal search capability of SA method. First, the single criterion of the mode calibration is employed to compare the performance of the evolutionary process of iteration with GA and chaos genetic algorithm (CGA). Then, the novel method together with fuzzy optimal model (FOM) is investigated for solving the multi-objective Xinanjiang model parameters calibration. Thirty-six historical floods with one-hour routing period for 5 years (2000–2004) in Shuangpai reservoir are employed to calibrate the model parameters whilst 12 floods in two recent years (2005–2006) are utilized to verify these parameters. The performance of the presented algorithm is compared with GA and CGA. The results show that the proposed hybrid algorithm performs better than GA and CGA.

Gene-expression programming to predict pier scour depth using laboratory data

Abstract: Prediction of bridge pier scour depth is essential for safe and economical bridge design. Keeping in mind the complex nature of bridge scour phenomenon, there is a need to properly address the methods and techniques used to predict bridge pier scour. Up to the present, extensive research has been carried out for pier scour depth prediction. Different modeling techniques have been applied to achieve better prediction. This paper presents a new soft computing technique called gene-expression programming (GEP) for pier scour depth prediction using laboratory data. A functional relationship has been established using GEP and its performance is compared with other artificial intelligence (AI)-based techniques such as artificial neural networks (ANNs) and conventional regression-based techniques. Laboratory data containing 529 datasets was divided into calibration and validation sets. The performance of GEP was found to be highly satisfactory and encouraging when compared to regression equations but was slightly inferior to ANN. This slightly inferior performance of GEP compared to ANN is offset by its capability to provide compact and explicit mathematical expression for bridge scour. This advantage of GEP over ANN is the main motivation for this work. The resulting GEP models will add to the existing literature of AI-based inductive models for bridge scour modeling.

Gene-expression programming to predict scour at a bridge abutment

Abstract: The process involved in the local scour at an abutment is so complex that it makes it difficult to establish a general empirical model to provide accurate estimation for scour. This study presents the use of gene-expression programming (GEP), which is an extension of genetic programming (GP), as an alternative approach to estimate the scour depth. The datasets of laboratory measurements were collected from the published literature and used to train the network or evolve the program. The developed network and evolved programs were validated by using the observations that were not involved in training. The proposed GEP approach gives satisfactory results compared with existing predictors and artificial neural network (ANN) modeling in predicting the scour depth at an abutment.

Improved methodology for processing raw LiDAR data to support urban flood modelling – accounting for elevated roads and bridges

Abstract: Digital Terrain Models (DTMs) represent an essential source of information that can allow the behaviour of the urban floodplain, and its interactions with the drainage system, to be examined, understood and predicted. Typically, such data are obtained via Light Detection and Ranging (LiDAR). If a DTM does not contain adequate representation of urban features the results from the modelling efforts can be. This is due to the fact that urban environments contain variety of features, which can have functions of storing and/or diverting flows during flood events. The work described in this paper concerns further improvements of a LiDAR filtering algorithm which was discussed in a previous work. The key characteristics of this improved algorithm are: ability to deal with buildings, detect elevated road and represent them accordance to reality and deal with bridges and riverbanks. The algorithm was tested using a real-life data from a case study of Kuala Lumpur. The results have shown that the newly developed MPMA2 algorithm has better capabilities of identifying some of the features that are vital for urban flood modelling applications than any of the currently available algorithms and it leads to better agreement between simulated and observed flood depths and flood extents.

Improving prediction accuracy of river discharge time series using a Wavelet-NAR artificial neural network

Abstract: This study developed a wavelet transformation and nonlinear autoregressive (NAR) artificial neural network (ANN) hybrid modeling approach to improve the prediction accuracy of river discharge time series. Daubechies 5 discrete wavelet was employed to decompose the time series data into subseries with low and high frequency, and these subseries were then used instead of the original data series as the input vectors for the designed NAR network (NARN) with the Bayesian regularization (BR) optimization algorithm. The proposed hybrid approach was applied to make multi-step-ahead predictions of monthly river discharge series in the Weihe River in China. The prediction results of this hybrid model were compared with those of signal NARNs and the traditional Wavelet-Artificial Neural Network hybrid approach (WNN). The comparison results revealed that the proposed hybrid model could significantly increase the prediction accuracy and prediction period of the river discharge time series in the current case study.

Generalizing WDN simulation models to variable tank levels

Abstract: In water distribution network (WDN) steady-state modelling, tanks and reservoirs are modelled as nodes with known heads. As a result, the tank levels are upgraded after every steady-state simulation (snapshot) using external mass balance equations in extended period simulation (EPS). This approach can give rise to numerical instabilities, especially when tanks are in close proximity. In order to obtain a stable EPS model, an unsteady formulation of the WDN model has recently introduced. This work presents an extension of the steady-state WDN model, both for demand-driven and pressure-driven analyses, allowing the direct prediction of head variation of tank nodes with respect to an initial state. Head variations at those nodes are introduced as internal unknowns in the model, the variation of tank levels can be analyzed in the single steady-state simulation and EPS can be performed as a sequence of simulations without the need for external mass balances. The extension of mass balance at tank nodes allows the analysis of some technically relevant demand components. Furthermore, inlet and outlet head losses at tank nodes are introduced and large cross-sectional tank areas are allowed by the model and reservoirs become a special case of tanks. The solution algorithm is the generalized-global gradient algorithm (G-GGA), although the proposed WDN model generalization is universal.

Prediction of dissolved oxygen in reservoirs using adaptive network-based fuzzy inference system

Abstract: Predicting water quality is the key factor in the water quality management of reservoirs. Since a large number of factors affect the water quality, traditional data processing methods are no longer good enough for solving the problem. The dissolved oxygen (DO) level is a measure of the health of the aquatic system and its prediction is very important. DO dynamics are highly nonlinear and artificial intelligence techniques are capable of modelling this complex system. The objective of this study was to develop an adaptive network-based fuzzy inference system (ANFIS) to predict the DO in the Gruža Reservoir, Serbia. The fuzzy model was developed using experimental data which were collected during a 3-year period. The input variables analysed in this paper are: water pH, water temperature, total phosphate, nitrites, ammonia, iron, manganese and electrical conductivity. The selection of an appropriate set of input variables is based on the building of ANFIS models for each possible combination of input variables. Results of fuzzy models are compared with measured data on the basis of correlation coefficient, mean absolute error and mean square error. Comparing the predicted values by ANFIS with the experimental data indicates that fuzzy models provide accurate results.

Statistical approaches and hydrochemical modelling of groundwater system in a small tropical island

Abstract: The aquifer of Manukan Island of Borneo, Malaysia had been found to be affected by seawater intrusion associated with excessive groundwater exploitation. This research attempted to characterize the chemistry of an impacted zone in the island using factor analysis (FA), cluster analysis (CA) and a hydrochemical model package (PHREEQC). The factor scores were used to plot the spatial map and to group the relationships among the monitoring wells using CA. The results of FA analysis revealed that the three main processes associated with the seawater intrusion event are aquifer salinization, cation exchange process and redox sequences. Output from the PHREEQC simulation was used to support the findings from the multivariate analysis.

Rainfall–runoff modelling using genetic programming

Abstract: This paper presents the application of genetic programming to the generation of models to assess the total runoff of a basin starting from the total rainfall in it and using data recorded in a sub-basin at the valley of Mexico (the Mixcoac sub-basin to the west of Mexico City). The modelling process is developed contrasting two types of models with different complexity degree: (1) a nonlinear model whose complexity is resolved using multi-objective optimization and (2) a nonlinear model with a given structure obtained by means of a physical interpretation of the dynamics of the direct and the base flow. Data from two storms (rainfall and runoff), one in 1997 and another in 1998, were used in testing the models. First, the storm in 1997 was used for the calibration step and that in 1998 for the validation step. Afterwards, the order was reversed. An interpretation of the results, focused on the applicability and possible improvement of the models in forecasting runoff, is made through their discussion and is summarized in the conclusions.

Novel simulation-based algorithms for optimal open-loop and closed-loop scheduling of deficit irrigation systems

Abstract: The scarcity of water compared with the abundance of land constitutes the main drawback within agricultural production. Besides the improvement of irrigation techniques a task of primary importance is solving the problem of intra-seasonal irrigation scheduling under limited seasonal water supply. An efficient scheduling algorithm has to take into account the crops9 response to water stress at different stages throughout the growing season. Furthermore, for large-scale planning tools compact presentations of the relationship between irrigation practices and grain yield, such as crop water production functions, are often used which also rely on an optimal scheduling of the considered irrigation systems. In this study, two new optimization algorithms for single-crop intra-seasonal scheduling of deficit irrigation systems are introduced which are able to operate with general crop growth simulation models. First, a tailored evolutionary optimization technique (EA) searches for optimal schedules over a whole growing season within an open-loop optimization framework. Second, a neuro-dynamic programming technique (NDP) is used for determining optimal irrigation policy. In this paper, different management schemes are considered and crop-yield functions generated with both the EA and the NDP optimization algorithms compared.

Comparison of four models to rank failure likelihood of individual pipes

Abstract: The use of statistical methods to discern patterns of historical breakage rates and use them to predict water main breaks has been widely documented. Particularly challenging is the prediction of breaks in individual pipes, due to the natural variations that exist in all the factors that affect their deterioration and subsequent failure. This paper describes alternative models developed into operational tools that can assist network owners and planners to identify individual mains for renewal in their water distribution networks. Four models were developed and compared: a heuristic model, a naive Bayesian classification model, a model based on logistic regression and finally a probabilistic model based on the non-homogeneous Poisson process (NHPP). These models rank individual water mains in terms of their anticipated breakage frequency, while considering both static (e.g. pipe material, diameter, vintage, surrounding soil, etc.) and dynamic (e.g. climate, operations, cathodic protection, etc.) effects influencing pipe deterioration rates.

Uncertainty-based automatic calibration of HEC-HMS model using sequential uncertainty fitting approach

Abstract: This study presents the application of an uncertainty-based technique for automatic calibration of the well-known Hydrologic Engineering Center-Hydrologic Modelling System (HEC-HMS) model. Sequential uncertainty fitting (SUFI2) approach has been used in calibration of the HEC-HMS model built for Tamar basin located in north of Iran. The basin was divided into seven sub-basins and three routing reaches with 24 parameters to be estimated. From the four events, three were used for calibration and one for verification. Each event was initially calibrated separately. As there was no unique parameter set identified, all events were then calibrated jointly. Based on the scenarios of separately and jointly calibrated events, different candidate parameter sets were inputted to the model verification stage where recalibration of initial abstraction parameters commenced. Some of the candidate parameter sets with no physically meaningful parameter values were withdrawn after recalibration. Then new ranges of parameters were identified based on minimum and maximum values of the remaining parameter sets. The new parameter ranges were used in an uncertainty analysis using SUFI2 technique resulting in much narrower parameter intervals that can simulate both verification and calibration events satisfactorily in a probabilistic sense. Results show that the SUFI2 technique linked to HEC-HMS as a simulation–optimization model can provide a basis for performing uncertainty-based automatic calibration of event-based hydrologic models.

A Methodology for Processing Raw LIDAR Data to Support Urban Flood Modelling Framework

Abstract: In the last few decades, the consequences of floods and flash floods in many parts of the world have been devastating. One way of improving flood management practice is to invest in data collection and modelling activities which enable an understanding of the functioning of a system and the selection of optimal mitigation measures. A Digital Terrain Model (DTM) provides the most essential information for flood manager. Light Detection and Ranging (LiDAR) surveys which enable the capture of spot heights at a spacing of 0.5m to 5m with a horizontal accuracy of 0.3m and a vertical accuracy of 0.15m can be used to develop high accuracy DTM but it need careful processing before it can be used for any application. The research presents the augmentation of an existing Progressive Morphological filtering algorithm for processing raw LiDAR data to support a 1D/2D urban flood modelling framework. The key characteristics of this improved algorithm are: (1) the ability to deal with different kinds of buildings; (2) the ability to detect elevated road/rail lines and represent them in accordance to the reality; (3) the ability to deal with bridges and riverbanks; and (4) the ability to recover curbs and the use of appropriated roughness coefficient of Manning’s value to represent close-to-earth vegetation (e.g. grass and small bush).

Computational decision analysis for flood risk management in an uncertain future

Abstract: Flood risk management is in many countries a major expense, and while the returns on this investment, in terms of risk reduction, are also high, the process of developing and choosing between management options is of critical importance. New sources of data and the falling cost of computation have made possible new approaches to options appraisal. The state of the art has a number of limitations, however. We present a comprehensive but parsimonious framework for computational decision analysis in flood risk management that addresses these issues. At its core is a simple but flexible model of change on the decadal time scale of typical option appraisals, including the management interventions that are the subject of decision along with influences, such as climate change, that are independent of the processes of flood risk management. A fully integrated performance model is developed, estimating both costs and benefits. Uncertainty analysis can thereby be applied to performance metrics of direct interest to stakeholders. We illustrate the framework with an implementation for a hypothetical flood risk management decision. We discuss possible variants of the framework that could be extended to fields other than flood risk management.

GroundWater Markup Language (GWML) - enabling groundwater data interoperability in spatial data infrastructures

Abstract: Increasing stress on global groundwater resources is leading to new approaches to the management and delivery of groundwater data. These approaches include the deployment of a Spatial Data Infrastructure (SDI) to enable online data interoperability amongst numerous and heterogeneous data sources.Oftenanimportant componentof anSDIisa globaldomainschema,whichservesasacentral structure for the query and transport of data, but at present there does not exist a schema for groundwater data that is strongly compliant with SDI concepts, standards, and technologies. In this paper we present GroundWater Markup Language (GWML), a groundwater application of the GeographyMarkupLanguage(GML).GWMLcanbeusedinconjunctionwithavarietyofwebservicesto

Calibration of a conceptual rainfall–runoff model using a genetic algorithm integrated with runoff estimation sensitivity to parameters

Abstract: This study proposes a parameter-calibration method (SA_GA) for the conceptual rainfall–runoff model using a real-value coding genetic algorithm (GA) which takes into account runoff estimation sensitivity to model parameters; this process is carried out using the standardized regression equation. The proposed SA_GA method treats the standardized values of model parameters as the real-value code and adopts a multinomial trial process with a probability of selecting genes for the crossover and mutation resulting from the runoff estimation sensitivity to the model parameters. A 19-parameter conceptual rainfall–runoff model, Sacramento Soil Moisture Accounting (SAC-SMA) model, and seven rainstorm events recorded in the Baj-Hang River watershed of South Taiwan are applied in the model development and validation. The results indicate that SA_GA is superior to a simple genetic algorithm (SGA) as regards the calculation of fi tness values associated with the optimal parameters under various GA operators. In addition, by comparing the performance indices of estimated runoff with the calibrated optimal parameters by SA_GA and SGA with the different number of calibration rainstorm events, SA_GA can provide efficient and robust optimal parameters. These parameters not only estimate reliable and accurate runoff, but also capture the varying trends of discharge in time.

Implementation of artificial neural network technique in the simulation of dam breach hydrograph

Abstract: In the present study, two artificial neural networks were developed to simulate outflow hydrograph from earthen dam breach. The required data for the modelling were collected from literature, laboratory experiments and a physically based model (i.e. BREACH). For the laboratory modelling, five different materials were used for the construction of different dams of various sizes, and the process of the breach was recorded by two video cameras to record the breach growth as well as the output hydrograph. The genetic algorithm was also applied to divide the data into three statistically similar sub-sets for training, validation and test purposes. The obtained results demonstrate that the results of the artificial neural network (ANN) method are in good agreement with the observed values, and this method produces better results than existing classical methods. Also, the experiments show when cohesive strength is larger, the breach process becomes slower, and the peak outflow and the final width and depth of breach become smaller. Moreover, when the friction angle is larger, the breach process becomes slower, and the peak outflow and the final width and depth of breach become smaller. However, the rate of breach formation is particularly dependent upon the soil properties.

Prediction of time variation of scour depth around spur dikes using neural networks

Abstract: The maximum depth of scouring around spur dikes plays an important role in the hydraulic design process. There have been many studies on the maximum depth of scouring, but there is little information available on the time variation of scour depth. In this paper, the time variation of scouring around the first spur dike in a series was investigated experimentally. Experiments were carried out in four different bed materials under different flow intensities (U/Ucr). To achieve a time development of scouring around the first spur dike, more than 750 sets of experimental data were collected. The results showed that 70–90% of the equilibrium scour depths were occurring during the initial 20% of the overall time of scouring. Based on the data analysis, a regression model and artificial neural networks (ANNs) were developed. The models were compared with other empirical equations in the literature. However, the results showed that the developed regression model is quite accurate and more practical, but the ANN models by feed forward back propagation and radial basis function provide a better prediction of observation. Finally, by sensitivity analysis, the most and the least effective parameters, which affected time variation of scouring, were determined.

Contribution of parallel NSGA-II in optimal design of water distribution networks

Abstract: Optimization of water distribution networks is a NP-hard problem that researchers have tried to deal with using different formulations and algorithmic approaches. Among these, multi-objective heuristic algorithms are interesting because of their capacity for dealing with separate objectives that allow us to choose a posteriori the best compromise, but one of their main drawbacks is the long time required to obtain good solutions. Parallel processing is the most promising way to reduce the computing time and can make the convergence to adequate solutions faster. This paper intends to investigate the possibility of improving the efficacy and efficiency of an NSGA-II algorithm by parallelization of the optimization process at the same time. Results of different parallel implementations of NSGA-II applied to optimal design of small- and medium-size water distribution networks are presented. Good speed-up can be reached with a global model, hence improving the algorithm efficiency. Unlike the global model, the island model (or the hierarchical parallelization) can also improve its efficacy because it introduces fundamental changes in the algorithm exploration method. Possibilities offered by parallel island models have been investigated showing that some parameter configurations can find better solutions compared with the serial version of the algorithm.

Principal component analysis of surface water quality data of the River Drava in eastern Croatia (24 year survey)

Abstract: The River Drava is one of the major, inexhaustible water sources not only for Croatia, but also for the other European countries it flows through. This study is based on the observations of 15 water variables at three sampling stations in the lower River Drava over a 24 year period. Although the obtained results revealed an improvement of most of the parameters, the values of some of them (i.e. NH4-N, NO3-N, BOD5, total coliform and heterotrophic bacteria) are still above the approved limits for water Class II. The results of principal component analysis (PCA) confirmed an existence of three clearly separated zones. The first zone corresponds to a rural upstream part of River Drava, which is characterised with low level pollution. The influences of untreated domestic waters become more noticeable in the second more densely populated suburban zone (II) located upstream of the city of Osijek. According to the results of the PCA, untreated wastewaters from Osijek are becoming contributing factors to the high pollution level of the river in the third (III) suburban zone. This study shows the usefulness of the PCA method for analysis and interpretation of complex data sets as well as for determination of pollution sources.

A well-balanced spectral volume scheme with the wetting-drying property for the shallow-water equations

Abstract: The shallow-water equations are widely used to model surface water bodies, such as lakes, rivers and the swash zone in coastal flows. Physically congruent solutions are characterized by non-negative water depth, and many numerical methods may fail to preserve this property at the discrete level when moving wet–dry transitions are present in the physical domain. In this paper, we present a spectral-volume method for the approximate solution of the one-dimensional shallow-water equations, which is third-order accurate in wet regions, far from discontinuities, and which is well balanced for water at rest states: the stability of the solution is ensured if reconstruction and limitation of variables preserves non-negativity of the depth and a suitable constraint for the time step length is satisfied. A number of numerical experiments are reported, showing the promising capabilities of the model to solve problems with non-trivial topographies and friction.

Multiscale nonlinear model for monthly streamflow forecasting: a wavelet-based approach

Abstract: The dynamics of the streamflow in rivers involve nonlinear and multiscale phenomena. An attempt is madetodevelopnonlinearmodelscombiningwaveletdecompositionwithVolterramodels.Thispaper describes a methodology to develop one-month-ahead forecasts of streamflow using multiscale nonlinear models. The method uses the concept of multiresolution decomposition using wavelets in order to represent the underlying integrated streamflowdynamics and this information, across scales, is then linked together using the first- and second-order Volterra kernels. The model is applied to 30 river data series from the western USA. The mean monthly data series of 30 rivers are grouped under the categories low, medium and high. The study indicated the presence of multiscale phenomena and discernable nonlinear characteristics in the streamflow data. Detailed analyses and results are presented only for three stations, selected to represent the low-flow, medium-flow and high-flow categories, respectively. The proposed model performance is good for all the flow regimes when compared with both the ARMA-type models as well as nonlinear models based on chaos theory.

Hybrid Pareto archived dynamically dimensioned search for multi-objective combinatorial optimization: application to water distribution network design

Abstract: Pareto archived dynamically dimensioned search (PA-DDS) has been modified to solve combinatorial multi-objective optimization problems. This new PA-DDS algorithm uses discrete-DDS as a search engine and archives all non-dominated solutions during the search. PA-DDS is also hybridized by a general discrete local search strategy to improve its performance near the end of the search. PA-DDS inherits the simplicity and parsimonious characteristics of DDS, so it has only one algorithm parameter and adjusts the search strategy to the user-defined computational budget. Hybrid PA-DDS was applied to five benchmark water distribution network design problems and its performance was assessed in comparison with NSGAII and SPEA2. This comparison was based on a revised hypervolume metric introduced in this study. The revised metric measures the algorithm performance relative to the observed performance variation across all algorithms in the comparison. The revised metric is improved in terms of detecting clear differences between approximations of the Pareto optimal front. Despite its simplicity, Hybrid PA-DDS shows high potential for approximating the Pareto optimal front, especially with limited computational budget. Independent of the PA-DDS results, the new local search strategy is also shown to substantially improve the final NSGAII and SPEA2 Pareto fronts with minimal additional computational expense.

A fuzzy approach for considering uncertainty in transient analysis of pipe networks

Abstract: Uncertain parameters in the transient analysis of pipe networks lead to uncertain responses. Typical uncertainties are nodal demand, pipe friction coefficient and wave speed, which not only are imprecise in nature but also change significantly over time. Exploiting the fuzzy set theory and a simple scheme of the simulated annealing method, a conceptual model is developed. It can take into account the uncertainties of conventional transient analysis. This model helps designers of pipe systems in finding out the extent to which uncertainties in the inputs can spread to the transient highest and lowest pressures. A real piping system is analyzed herein as the case study. The results show that the transient extreme pressures can be highly affected by the uncertainties.