Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks

The success of hydraulic simulation models of water distribution networks is associated with the ability of these models to represent real systems accurately. To achieve this, the calibration phase is essential. Current calibration methods are based on minimizing the error between measured and simulated values of pressure and flow. This minimization is based on a search of parameter values to be calibrated, including pipe roughness, nodal demand, and leakage flow. The resulting hydraulic problem contains several variables. In addition, a limited set of known monitored pressure and flow values creates an indeterminate problem with more variables than equations. Seeking to address the lack of monitored data for the calibration of Water Distribution Networks (WDNs), this paper uses a meta-model based on an Artificial Neural Network (ANN) to estimate pressure on all nodes of a network. The calibration of pipe roughness applies a metaheuristic search method called Particle Swarm Optimization (PSO) to minimize the objective function represented by the difference between simulated and forecasted pressure values. The proposed method is evaluated at steady state and over an extended period for a real District Metering Area (DMA), named Campos do Conde II, and the hypothetical network named C-town, which is used as a benchmark for calibration studies.     

Sustainable Basin-Scale Water Allocation with Hydrologic State-Dependent Multi-Reservoir Operation Rules

This study extends the PSO-MODSIM model, integrating particle swarm optimization (PSO) algorithm and MODISM river basin decision support system (DSS) to determine optimal basin-scale water allocation, in two aspects. The first is deriving hydrologic state-dependent (conditional) operating rules to better account for drought and high-flow periods, and the second is direct, explicit consideration of sustainability criteria in the model’s formulation to have a better efficiency in basin-scale water allocation. Under conditional operating rules, the operational parameters of reservoir target storage levels and their priority rankings were conditioned on the hydrologic state of the system in a priority-based water allocation scheme. The role of conditional operating rules and policies were evaluated by comparing water shortages associated with objective function values under unconditional and conditional operating rules. Optimal basin-scale water allocation was then evaluated by incorporating reliability, vulnerability, reversibility and equity sustainability indices into the PSO objective function. The extended model was applied for water allocation in the Atrak River Basin, Iran. Results indicated improved distribution of water shortages by about 7.5% using conditional operating rules distinguishing dry, normal and wet hydrologic states. Alternative solutions with nearly identical objective function values were found with sustainability indices included in the model.     

Medium-Term Hydro Generation Scheduling (MTHGS) with Chance Constrained Model (CCM) and Dynamic Control Model (DCM)

Medium-Term Hydro Generation Scheduling (MTHGS) plays an important role in the operation of hydropower systems. In the first place, this paper presents a Chance Constrained Model for solving the optimal MTHGS problem. The model recognizes the impact of inflow uncertainty and the constraints involving hydrologic parameters subjected to uncertainty are described as probabilistic statements. It aims at providing a more practical technique compared to the traditional deterministic approaches used for MTHGS. The stochastic inflow is expressed as a simple discrete-time Markov chain and Stochastic Dynamic Programming is adopted to solve the model. Then in order to use the information of long-term inflow forecast to improve dispatching decisions, a Dynamic Control Model is developed. Short-term forecast results of the current period and long-term forecast results of the remaining period are treated as inputs of the model. Finally, the two methods are applied to MTHGS of Xiluodu hydro plant in China. The results are compared to those obtained from Deterministic Dynamic Programming with hindsight and advantages and disadvantages of the two methods are analyzed.     

Informational Entropy: a Failure Tolerance and Reliability Surrogate for Water Distribution Networks

Evolutionary algorithms are used widely in optimization studies on water distribution networks. The optimization algorithms use simulation models that analyse the networks under various operating conditions. The solution process typically involves cost minimization along with reliability constraints that ensure reasonably satisfactory performance under abnormal operating conditions also. Flow entropy has been employed previously as a surrogate reliability measure. While a body of work exists for a single operating condition under steady state conditions, the effectiveness of flow entropy for systems with multiple operating conditions has received very little attention. This paper describes a multi-objective genetic algorithm that maximizes the flow entropy under multiple operating conditions for any given network. The new methodology proposed is consistent with the maximum entropy formalism that requires active consideration of all the relevant information. Furthermore, an alternative but equivalent flow entropy model that emphasizes the relative uniformity of the nodal demands is described. The flow entropy of water distribution networks under multiple operating conditions is discussed with reference to the joint entropy of multiple probability spaces, which provides the theoretical foundation for the optimization methodology proposed. Besides the rationale, results are included that show that the most robust or failure-tolerant solutions are achieved by maximizing the sum of the entropies.     

Multiple Random Forests Modelling for Urban Water Consumption Forecasting

The precise forecasting of water consumption is the basis in water resources planning and management. However, predicting water consumption fluctuations is complicated, given their non-stationary and non-linear characteristics. In this paper, a multiple random forests model, integrated wavelet transform and random forests regression (W-RFR), is proposed for the prediction of daily urban water consumption in southwest of China. Raw time series were first decomposed into low- and high-frequency parts with discrete wavelet transformation (DWT). The random forests regression (RFR) method was then used for prediction using each subseries. In the process, the input and output constructions of the RFR model were proposed for each subseries on the basis of the delay times and the embedding dimension of the attractor reconstruction computed by the C-C method, respectively. The forecasting values of each subseries were summarized as the final results. Four performance criteria, i.e., correlation coefficient (R), mean absolute percentage error (MAPE), normalized root mean square error (NRMSE) and threshold static (TS), were used to evaluate the forecasting capacity of the W-RFR. The results indicated that the W-RFR can capture the basic dynamics of the daily urban water consumption. The forecasted performance of the proposed approach was also compared with those of models, i.e., the RFR and forward feed neural network (FFNN) models. The results indicated that among the models, the precision of the predictions of the proposed model was greater, which is attributed to good feature extractions from the multi-scale perspective and favorable feature learning performance using the decision trees.     

Comprehensive Benefit Evaluation System for Low-Impact Development of Urban Stormwater Management Measures

In recent years, plenty of simulation research about the low impact development(LID) control effect has emerged, but studies on scheme comparison and evaluation are lacking. In this study, a comprehensive benefit evaluation system for LID, including environmental, economic, and social benefits, was established on the basis of Analytic Hierarchy Process(AHP) and urban storm water model. Above all, benefit identification, quantitative evaluation and scheme comparison of single LID measures were obtained according to site investigation, simulated calculation and theoretical analysis. Whereafter, LID combination plans were designed based on single LID measures with high comprehensive benefit values, and their comprehensive benefits were evaluated to obtain the optimal plan. Then, based on well-founded system combined with Storm Water Management Model (SWMM), the design and optimization of LID were conducted, with a case in Xi’an, China. It turned out that the preferential order of the LID single measures according to the comprehensive benefit was: bio-retention > rain barrels > low-elevation greenbelt > green roofs > permeable pavement. Five LID combination plans were designed based on bio-retention, rain barrels, low-elevation greenbelt, and green roofs. Evaluation results showed that plan I (bio-retention and green roofs) was the optimal LID combination plan.     

Flood Forecasting and Decision Making in the new Millennium. Where are We?

This paper reviews the development of real time flood forecasting systems from the early 1970 approaches to the recent probabilistic ones. A preliminary discussion on the motivations for developing real time flood forecasting systems is introduced to explain their evolution in the last four to five decades. It will be shown how recent probabilistic flood forecasts are more robust and effective than the traditional deterministic ones. In particular, when combined with Bayesian decision approaches, probabilistic forecasts are the most appropriate tools for rational decision making in flood warning and flood management.Moreover, they allow taking into account the information from several models to be taken into account by combining into a unique predictive density the deterministic predictions of several hydrological or hydraulic models of a different nature, while in the multi-temporal forecasting extensions, they provide to answers questions such as: Which is the probability of overtopping a dyke in the next 24 h? When will this event be more likely to occur during the next 24 h?The work concludes with a discussion on the still unresolved problems, namely how decisions makers can fully take advantage of the probabilistic forecasts and how these forecasts must be communicated to them in order to meet this objective.     

Participatory Modeling Workshops in a Water-Stressed Basin Result in Gains in Modeling Capacity but Reveal Disparity in Water Resources Management Priorities

Participatory modeling workshops were held in Sonora, México, with the goal of developing water resources management strategies in a water-stressed basin. A model of the water resources system, consisting of watershed hydrology, water resources infrastructure, and groundwater models, was developed deliberatively in the workshops, along with scenarios of future climate and development. Participants used the final version of the water resources systems model to select management strategies. The performance of the strategies was based on the reliability of meeting current and future demands at a daily time scale over a year’s period. Pre- and post-workshop surveys were developed and administered. The survey questions focused on evaluation of participants’ modeling capacity and the utility and accuracy of the models. The selected water resources strategies and the associated, expected reliability varied widely among participants. Most participants could be clustered into three groups with roughly equal numbers of participants that varied in terms of reliance on expanding infrastructure vs. demand modification; expectations of reliability; and perceptions of social, environmental, and economic impacts. The wide range of strategies chosen and associated reliabilities indicate that there is a substantial degree of uncertainty in how future water resources decisions could be made in the region. The pre- and post-survey results indicate that participants believed their modeling abilities increased and beliefs in the utility of models increased as a result of the workshops.     

Experimental Investigation of Epoxy Bonded Polymethylmethacrylate Joints

This paper presents an experimental investigation into various aspects of epoxy-bonded polymethylmethacrylate (PMMA) and PMMA-to-aluminium joints. The effects of adhesive thickness, overlap area, surface roughness, and environmental exposure on the joint strength were studied. Results indicated that the joint strength was not directly proportional to the overlap area, while sanding had a positive effect on the joint strength. A negative effect was observed when adhesive thickness was increased. The fatigue behaviour of adhesively-bonded joints under dynamic loading was found to be independent of frequency, for the range of values tested; however, it was dependent on the test temperature with greater reduction in fatigue life observed in PMMA-to-aluminium joints at higher temperature. Empirical equations from which the fatigue life of joints can be predicted were obtained by regression analysis. Intermittent fatigue testing of the joints was also performed. The epoxy adhesive tested proved to be a satisfactory choice for outdoor exposure. The rate of degradation of the adhesive was slow with the adherend itself degrading at a faster rate than the adhesive or the bondline.