Parameterization of a Gridded Rainfall-Runoff Model for Southern Australia

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

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

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

State updating and calibration period selection to improve dynamic monthly streamflow forecasts for an environmental flow management application

Monthly to seasonal streamflow forecasts provide useful information for a range of water resource management and planning applications. This work focuses on improving such forecasts by considering the following two aspects: (1) state updating to force the models to match observations from the start of the forecast period, and (2) selection of a shorter calibration period that is more representative of the forecast period, compared to a longer calibration period traditionally used. The analysis is undertaken in the context of using streamflow forecasts for environmental flow water management of an open channel drainage network in southern Australia. Forecasts of monthly streamflow are obtained using a conceptual rainfall–runoff model combined with a post-processor error model for uncertainty analysis. This model set-up is applied to two catchments, one with stronger evidence of non-stationarity than the other. A range of metrics are used to assess different aspects of predictive performance, including reliability, sharpness, bias and accuracy. The results indicate that, for most scenarios and metrics, state updating improves predictive performance for both observed rainfall and forecast rainfall sources. Using the shorter calibration period also improves predictive performance, particularly for the catchment with stronger evidence of non-stationarity. The results highlight that a traditional approach of using a long calibration period can degrade predictive performance when there is evidence of non-stationarity. The techniques presented can form the basis for operational monthly streamflow forecasting systems and provide support for environmental decision-making.Matthew S. Gibbs, David McInerney, Greer Humphrey, Mark A. Thyer, Holger R. Maier, Graeme C. Dandy and Dmitri Kavetsk

Microsurgical third ventriculocisternostomy as an alternative to ETV: report of two cases

OBJECTIVE: To describe a microsurgical alternative to endoscopic third ventriculocisternostomy. METHODS: Two children with shunt-dependent hydrocephalus and multiple shunt revisions were considered candidates for third ventriculocisternostomy (TVS). Because of slit ventricles, an endoscopic approach was not possible and, therefore, both patients received a microsurgical TVS by a supraorbital approach. RESULTS: In both cases, microsurgical TVS was successful and the patients became shunt free. CONCLUSION: Microsurgical TVS by a supraorbital craniotomy is a viable alternative to endoscopic TVS in selected cases

Paradigm shift in hydrocephalus research in legacy of Dandy’s pioneering work: rationale for third ventriculostomy in communicating hydrocephalus

OBJECTIVE: This study aims to question the generally accepted cerebrospinal fluid (CSF) bulk flow theory suggesting that the CSF is exclusively absorbed by the arachnoid villi and that the cause of hydrocephalus is a CSF absorption deficit. In addition, this study aims to briefly describe the new hydrodynamic concept of hydrocephalus and the rationale for endoscopic third ventriculostomy (ETV) in communicating hydrocephalus. CRITIQUE: The bulk flow theory has proven incapable of explaining the pivotal mechanisms behind communicating hydrocephalus. Thus, the theory is unable to explain why the ventricles enlarge, why the CSF pressure remains normal and why some patients improve after ETV. HYDRODYNAMIC CONCEPT OF HYDROCEPHALUS: Communicating hydrocephalus is caused by decreased intracranial compliance increasing the systolic pressure transmission into the brain parenchyma. The increased systolic pressure in the brain distends the brain towards the skull and simultaneously compresses the periventricular region of the brain against the ventricles. The final result is the predominant enlargement of the ventricles and narrowing of the subarachnoid space. The ETV reduces the increased systolic pressure in the brain simply by venting ventricular CSF through the stoma. The patent aqueduct in communicating hydrocephalus is too narrow to vent the CSF sufficiently