01 New York Tunnel

The New York Tunnel system is based on the real-world transmission system in New York City and was created by Schaake & Lai in 1969 as part of a study to optimize the duplication of the existing system to meet demand increases. The system has a total demand of 1305 MGD, one reservoir and 21 tunnels with a total length of 69.2 miles. It is classified as distribution dense-grid by Hwang & Lansey (2017) and gridded by Hoagland et al. (2015).https://uknowledge.uky.edu/wdst_systems/1000/thumbnail.jp

04 Jilin

The Jilin system is a synthetic system and was originally developed by Bi & Dandy in 2014 as part of a study on online retrained metamodels. The system has a total demand of 112,000 CMD, one reservoir, and 29 km of pipe. It is classified as distribution dense-grid by Hwanyg & Lansey (2017) and gridded by Hoagland et al. (2015).https://uknowledge.uky.edu/wdst_synthetic/1000/thumbnail.jp

03 KL

The KL system is a synthetic system and was originally developed by Kang & Lansey in 2012 as part of a study on the heuristic hierarchical approach to optimization. The system has a total demand of 4.0 MGD, one reservoir, and 157 miles of pipe. It is classified as distribution dense-grid by Hwang & Lansey (2017) and looped by Hoagland et al. (2015).https://uknowledge.uky.edu/wdst_synthetic/1001/thumbnail.jp

07 Hanoi System

The Hanoi system is a based on the planned trunk network of Hanoi, Vietnam and was originally used by Fujiwara & Khang in 1990 to test pipe size optimization software. The system has a total demand of 126.5 CMD, one reservoir and 39 km of pipe. It is classified as transmission dense-loop by Hwang & Lansey (2017) and looped by Hoagland et al. (2015).https://uknowledge.uky.edu/wdst_systems/1001/thumbnail.jp

An evaluation framework for input variable selection algorithms for environmental data-driven models

Abstract not availableStefano Galelli, Greer B. Humphrey, Holger R. Maier, Andrea Castelletti, Graeme C. Dandy, Matthew S. Gibb

Improved validation framework and R-package for artificial neural network models

Validation is a critical component of any modelling process. In artificial neural network (ANN) modelling, validation generally consists of the assessment of model predictive performance on an independent validation set (predictive validity). However, this ignores other aspects of model validation considered to be good practice in other areas of environmental modelling, such as residual analysis (replicative validity) and checking the plausibility of the model in relation to a priori system understanding (structural validity). In order to address this shortcoming, a validation framework for ANNs is introduced in this paper that covers all of the above aspects of validation. In addition, the validann R-package is introduced that enables these validation methods to be implemented in a user-friendly and consistent fashion. The benefits of the framework and R-package are demonstrated for two environmental modelling case studies, highlighting the importance of considering replicative and structural validity in addition to predictive validity

A Comparison of Sensitivity Analysis Techniques for Complex Models for Environmental Management

Computer based modelling methods are being used increasingly to replicate natural systems in order to review both large and small scale policy measures prior to their implementation. Integrated Assessment Modelling (IAM) incorporates knowledge from several different disciplines into one model in order to provide an overarching assessment of the impact of different management decisions. The importance of IAM is that the environmental, social and economic impacts of management choices can be assessed within a single model, further allowing assessment in relation to sustainability criteria. The considerable detail facilitated by these models often requires the inclusion of a large number of parameters and model inputs, many of whose values may not be known with certainty. For this reason and because models do not always behave intuitively (in particular when there are non-linearities involved), sensitivity analysis (SA) of the model to changes in its parameters and inputs is an important stage of model development. Current SA methods have not kept pace with rapid increases in computing power and availability and more importantly the resultant increases in model size and complexity. Also related to the complexity is increased difficulty in finding and fitting distributions to all parameters. Further, the complex nature of integrated models requires SA that is flexible and can be implemented regardless of model structure. This research aims to establish new criteria for SA used in the context of integrated models for environmental management and decision-making. These criteria are believed to reflect the current requirements specific to this type of modelling. Desirable criteria are identified as: high computational efficiency; ability to take into account higher order parameter interactions; ability to account for model non-linearities; not requiring knowledge of parameter probability distributions; and use in decision making. SA of an integrated model of the Namoi River catchment is performed using the Fourier Amplitude Sensitivity Testing (FAST) method, Morris method, method of Sobol', and regression and correlation coefficients. The results from these analyses are used as a basis for comparing the SA methods by the new criteria outlined above. The Namoi model is a combination of a flow model with a non-linear component, a policy model, an economic model and an extraction model. It can be used for assessing management options for the river. SA of two different potential management options for the catchment is undertaken to facilitate comparison of sensitivity between two slightly different models. Comparison of the different SA methods shows that none of the methods meet all of the criteria and, in particular, there are no methods that are effective for use when comparing management options. This lack of an adequate SA method for integrated models indicates that development of a new method of SA specifically for integrated models for environmental management is desirable. The FAST method is shown to meet the criteria most effectively, being able to account for model non-linearity and non-monotonicity, requiring only parameter ranges (not distributions), and being relatively computationally efficient (although this does come at a loss of some resolution). Results from the FAST SA of the Namoi model show the model to be sensitive to several parameters within the non-linear loss module. Further, one management option shows sensitivity to the decision variables within the model while the other does not. This means that the first management option clearly corresponds to the more controllable form of the model

Optimal division of data for neural network models in water resources applications

The way that available data are divided into training, testing, and validation subsets can have a significant influence on the performance of an artificial neural network (ANN). Despite numerous studies, no systematic approach has been developed for the optimal division of data for ANN models. This paper presents two methodologies for dividing data into representative subsets, namely, a genetic algorithm (GA) and a self-organizing map (SOM). These two methods are compared with the conventional approach commonly used in the literature, which involves an arbitrary division of the data. A case study is presented in which ANN models developed using each data division technique are used to forecast salinity in the River Murray at Murray Bridge (South Australia) 14 days in advance. When tested on a validation data set from July 1992 to March 1998, the models developed using the GA and SOM data division techniques resulted in a reduction in RMS error of 24.2% and 9.9%, respectively, over the conventional data division method. It was found that a SOM could be used to diagnose why an ANN model has performed poorly, given that the poor performance is primarily related to the data themselves and not the choice of the ANN's parameters or architecture.Gavin J. Bowden, Holger R. Maier and Graeme C. Dand