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This paper investigates the influence of different hydrological input data on the management simulation of a water resources system (WRS). Three complete simulations were carried out using synthetic inflow series generated with several stochastic models: an autoregressive moving average (ARMA) model, the Lane condensed temporal disaggregation model, and a nonlinear model based on a multilayer perceptron artificial neural network (MLP-ANN) with a random component embedded. The validation of the stochastic models was performed using comparisons of relevant drought statistics from synthetic series with those from the historical records. Since the analyzed WRS includes five inflow sites, multivariate models were applied. The MLP-ANN model showed the best performance. The management simulations of the WRS were executed with the decision support system AQUATOOL under a probabilistic approach. This approach gives probabilities of demand failures of the WRS, which were used to evaluate the influence of the three applied stochastic models on the simulation results. Significant differences were found. [ABSTRACT FROM AUTHOR]

Based on the basic formulation developed in a companion paper, the writers now present the application of an artificial neural network approach to designing streamlined network models to simulate and identify the nonlinear dynamic response of single-degree-of-freedom oscillators using the restoring force-state mapping interpretation. The neural networks which use sigmoidal activation functions are shown to be highly robust in modeling a wide variety of commonly observed nonlinear structural dynamic response behaviors. By streamlining the networks, individual network model parameters take on physically or geometrically interpretable meaning, and hence, the network initialization can be achieved through an engineered approach rather than through less physically meaningful numerical initialization schemes. Although not proven in general, examples show that by starting with a more meaningful initial design, identification convergence is improved, and the final identified model parameters are seen to have a more physical meaning. A set of model architecture prototypes is developed to capture commonly observed nonlinear response behaviors. [ABSTRACT FROM AUTHOR]

Modernization of today's irrigation systems attempts to improve system efficiency and management effectiveness of every component of the system (reservoirs, canals, and gates) using automation technologies, along with hydraulic simulation models. The canal flow control scheme resulting from the coupling of the system automation and the simulation models has proven to be an excellent irrigation water management instrument around the world. Nevertheless, the harsh environment of irrigation systems can induce uncertainties or errors in the components of canal flow control that can worsen over time, misleading or confusing both human and computer controllers. These errors can be attributed to parameter measurement and conceptual sources, with the complexity of locating their individual origin. In this paper, a framework is presented to minimize the collective or aggregate error within an irrigation canal flow control scheme that uses a learning machine algorithm (multilayer perceptron and relevance vector machine) embedded in a hydraulic simulation model fed by a canal automation system. This framework is evaluated using actual data from an irrigation conveyance canal located at the Lower Sevier River Basin in Utah. The results obtained prove the adequacy of the proposed framework in minimizing the aggregate error, which affects the simulation results of the automation system (up to 91% in bias and 83% in maximum absolute error) when compared with the original values obtained in the verification period. The temporal correlation of the aggregate error was also significantly reduced, thus resulting in reduced local biases and structures in the model prediction error. [ABSTRACT FROM AUTHOR]

A linked simulation-optimization model using artificial neural networks (ANNs) and genetic algorithms (GAs) is developed for deriving multiple objective management strategies for coastal aquifers. The GA-based optimization approach is especially suitable for externally linking a numerical simulation model within the optimization model. However, the solution of a linked simulation-optimization model is computationally intensive, as a very large number of iterations between the optimization and the simulation models are necessary to arrive at an optimal management strategy. Computational efficiency and feasibility for such linked models can be enhanced by simplifying the simulation process by an approximation. A possible approach for such approximation is the use of an ANN model. In this paper, an ANN model is developed initially as an approximate simulator of the three-dimensional density dependent flow and transport processes in a coastal aquifer. A simulation-optimization model is then developed by linking the ANN model with a GA-based optimization model for solving multiple objective saltwater management problems. The performance of the optimization models is evaluated using an illustrative study area. For comparison of the solution results, a multiple objective management model is also solved using embedded formulation and classical nonlinear optimization technique. The comparison of results shows potential feasibility of the proposed methodology in solving multiple objective management model for coastal aquifers. [ABSTRACT FROM AUTHOR]

This paper addresses the modeling problem of nonlinear and hysteretic dynamic behaviors through a constructive modeling approach which exploits existing mathematical concepts in artificial neural network modeling. In contrast with many neural network applications, which often result in large and complex “black-box” models, here, the writers strive to produce phenomenologically accurate model behavior starting with network architecture of manageable/small sizes. This affords the potential of creating relationships between model parameter values and observed phenomenological behaviors. Here a linear sum of basis functions is used in modeling nonlinear hysteretic restoring forces. In particular, nonlinear sigmoidal activation functions are chosen as the core building block for their robustness in approximating arbitrary functions. The appropriateness and effectiveness of this set of basis function in modeling a wide variety of nonlinear dynamic behaviors observed in structural mechanics are depicted from an algebraic and geometric perspective. [ABSTRACT FROM AUTHOR]

The world population is increasing continuously and is expected to reach the 9.5 billion mark in 2050 from the current 7.1 billion. The importance of the groundwater resources is also increasing with the increase in population, because the quality and quantity of water resources are continuously declining because of urbanization, contamination, and climate change impact. Thus, under the current environment, the conservation and management of groundwater resources is a critical challenge for fulfilling the rising water demand for agricultural, industrial, and domestic uses. Various simulation and optimization approaches have been used to solve the groundwater management problems. Optimization methods have proved to be of great importance when used with simulation models, and the combined use of these two approaches gives the best results. The main objective of this review is to analyze combined uses of simulation and optimization modeling approaches and to provide an impression of their applications reported in literature. In addition to traditional optimization techniques, this paper highlights the application of computational intelligence techniques, such as artificial neural networks, response matrix approach, and multiobjective approach. Conclusions are drawn based on this review, which could be useful for system managers and planners for selecting the best suitable technique for their specific uses. [ABSTRACT FROM AUTHOR]

The current study was undertaken to analyze the performance of three back-propagation training algorithms of artificial neural network (ANN) along with a multiple linear regression model (MLR) for transient simulation of short/midterm soil temperature (TS). Each ANN used a different type of learning algorithm (gradient descent, conjugate gradient, and Levenberg-Marquardt) as methods for daily and weekly TS prediction. The analysis was performed as a case study using three meteorological parameters [air temperature (TA), net radiation (NR), and relative humidity (RH)] and two hydrological variables [precipitation (P) and runoff (Q)] as input data for a region in Wyoming. Pearson and cross correlation analyses were applied to investigate the relationships between input and target values to determine several input combinations. The correlation analysis indicated that the meteorological parameters of TA and NR were reasonably correlated with TS. Based on the input combinations, 14 models were constructed for each of the daily/weekly MLR and ANNs. The accuracy of the predictions was evaluated by the RMS error (RMSE), mean absolute error (MAE), and Nash-Sutcliffe coefficients between the measured and predicted TS values. The Levenberg-Marquardt ANN method was found to provide a more accurate prediction than the other two types of ANNs and the MLR model. Furthermore, it was found that weekly structure of ANNs performance surpasses that of any other daily structures. Although results illustrated that the soil temperature is a function of meteorological variables, the measured TS is also shown to be positively influenced by hydrological parameters-a fact that has not been pointed out by correlation analyses. [ABSTRACT FROM AUTHOR]

Optimization of large and hydraulically complex water distribution systems (WDSs) is computationally expensive as simulation models are required to evaluate the performance of solutions to the problem at hand. Metamodels can act as a surrogate or substitute for these simulation models and provide significant speed-ups in the optimization process. The application of metamodels in the field of WDS optimization has been limited to date, and little guidance has been given in terms of constructing metamodels for hydraulically complex systems. While it is relatively straightforward to obtain satisfactory metamodel approximations to simulation models of simple WDSs, this is not necessarily the case for more complex networks. In order to reduce the complexity of the relationship that is to be approximated by the metamodels, a number of factors have to be considered, including the complexity of the hydraulic simulation model, the complexity of the decision space, and the locations at which outputs are required from the hydraulic simulation model. This research presents a systematic methodology for dealing with these factors and demonstrates the effectiveness of the approach by applying it to an actual WDS. A system in Wallan, Victoria, Australia, is selected for demonstration purposes. Four different metamodelling scenarios are presented here. The results show that, for this case study, some skeletonization of the model is required to achieve suitably accurate metamodels. The optimization results show a reduction in the average daily pumping costs from $457 to $363; a saving of 21%. The net present value (NPV) over 25 years is used as the objective function, which includes both pumping and chlorine costs. The current operating regime corresponds to an NPV of $1.56 million, while the optimized solution has an NPV of $1.34 million; a saving of 14%. In addition to these economic benefits, the optimized solution achieves adequate disinfection throughout the system, whereas the current operating regime results in deficits in chlorine residuals at several locations in the system. [ABSTRACT FROM AUTHOR]

Generalized models for predicting the storage–yield–reliability functions of surface water reservoirs are developed using multiple linear regression and multilayer perceptron, artificial neural networks (ANNs). Linear regression was used to model the total capacity using the over-year capacity as one of the inputs. However, the ANNs were used to simultaneously model directly the intrinsically nonlinear over-year and total (i.e., within-year+over-year) capacity-yield-reliability functions. The inputs used for the ANNs were basic runoff and systems variables such as the coefficient of variation of annual and monthly runoff, minimum monthly runoff, the demand ratio, and reliability. The results showed that all the models performed well during development and when tested with independent data sets. Both models offer avenues for predicting reservoir capacity at gauged sites without the expense of time-series based simulation alternatives. [ABSTRACT FROM AUTHOR]

An optimal groundwater remediation design problem generally requires determination of the location of extraction and injection wells and their pumping and injection rates once the well locations are selected. According to many researchers, the determination of an optimal well location is even more important than the optimal pumping rate in groundwater remediation problems. The objective of the study is to apply a neural network embedded Monte Carlo approach to determine potential well locations for in situ bioremediation of contaminated groundwater. The methodology developed in this study has three important components: a bioremediation simulation model, an artificial neural network, and an application of Monte Carlo simulations. This method has been further applied to an in situ bioremediation problem, for which data are adopted from the available literature. The results show that the proposed approach can successfully identify potential well locations from a set of preselected well locations, which can be further used in optimization algorithms to identify optimal well locations and the corresponding pumping rates. The advantage of this approach is that it reduces the size of the problem considerably by eliminating redundant well locations and hence the computational burden involved. Furthermore, the computational burden of Monte Carlo simulations is managed within a practical time frame by replacing the bioremediation simulation model with a trained neural network. [ABSTRACT FROM AUTHOR]

This study presents the development of artificial neural network (ANN) and fuzzy logic (FL) models for predicting event-based rainfall runoff and tests these models against the kinematic wave approximation (KWA). A three-layer feed-forward ANN was developed using the sigmoid function and the backpropagation algorithm. The FL model was developed employing the triangular fuzzy membership functions for the input and output variables. The fuzzy rules were inferred from the measured data. The measured event based rainfall-runoff peak discharge data from laboratory flume and experimental plots were satisfactorily predicted by the ANN, FL, and KWA models. Similarly, all the three models satisfactorily simulated event-based rainfall-runoff hydrographs from experimental plots with comparable error measures. ANN and FL models also satisfactorily simulated a measured hydrograph from a small watershed 8.44 km2 in area. The results provide insights into the adequacy of ANN and FL methods as well as their competitiveness against the KWA for simulating event-based rainfall-runoff processes. [ABSTRACT FROM AUTHOR]