Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 4 of 4
Full-Text Articles in Entire DC Network
A Statistical Input Pruning Method For Artificial Neural Networks Used In Environmental Modelling, G. B. Kingston, Holger R. Maier, M. F. Lambert
A Statistical Input Pruning Method For Artificial Neural Networks Used In Environmental Modelling, G. B. Kingston, Holger R. Maier, M. F. Lambert
International Congress on Environmental Modelling and Software
Artificial neural networks (ANNs) provide a useful and effective tool for modelling poorly understood and complex processes, such as those that occur in nature. However, developing an ANN to properly model the desired relationship is not a trivial task. Selection of the correct causal inputs is one of the most important tasks faced by neural network practitioners, but as knowledge regarding the relationships modelled by ANNs is generally limited, selecting the appropriate inputs is also one of the most difficult tasks in the development of an ANN. Many of the methods available for assessing the significance of potential input variables …
A Fast Evolutionary-Based Meta-Modelling Approach For The Calibration Of A Rainfall-Runoff Model, Soon-Thiam Khu, Dragan Savic, Yang Liu, Henrik Madsen
A Fast Evolutionary-Based Meta-Modelling Approach For The Calibration Of A Rainfall-Runoff Model, Soon-Thiam Khu, Dragan Savic, Yang Liu, Henrik Madsen
International Congress on Environmental Modelling and Software
Population-based search methods such as evolutionary algorithm, shuffled complex algorithm, simulated annealing and ant colony search are increasing used as automatic calibration methods for a wide range of water and environmental simulation models. However, despite the advances in computer power, it may still be impractical to rely exclusively on computationally expensive (time consuming) simulation for many real world complex problems. This paper proposed the use of meta-models to replace numerical simulation models for the purpose of calibration. Meta-models are essentially “model of the model”. The meta-model used in this study is the artificial neural network and, when coupled with genetic …
An Evolutionary-Based Real-Time Updating Technique For An Operational Rainfall-Runoff Forecasting Model, Soon-Thiam Khu, Edward Keedwell, Oliver Pollard
An Evolutionary-Based Real-Time Updating Technique For An Operational Rainfall-Runoff Forecasting Model, Soon-Thiam Khu, Edward Keedwell, Oliver Pollard
International Congress on Environmental Modelling and Software
Error-correction is widely known to be one of the effective methods of real-time updating and tends to be the easiest method to implement and couple with existing simulation models. Methods such as autoregressive (AR) or autoregressive integrated moving average (ARIMA) have been widely used but the main disadvantage of such approaches is the prior assumption of the form of error correlation. Genetic programming (GP), a relatively new evolutionary-based technique, can be used to generate a suitable expression linking the observations, simulation model results and the error in the simulation for the purpose of error correction. In this study, GP functions …
Benthic Macroinvertebrates Modelling Using Artificial Neural Networks (Ann): Case Study Of A Subtropical Brazilian River, D. Pereiraa
International Congress on Environmental Modelling and Software
Back-propagation Artificial Neural Networks (ANN) were tested with the aim of modelling the occurrence of benthic macroinvertebrate families in a south Brazilian river. The dataset, consisting of 67 sets of observations of macroinvertebrate abundance (families Hydrobiidae, Tubificidae, Chironomidae, Baetidae and Leptophlebiidae) and water quality variables (pH, temperature, dissolved oxygen, biochemical oxygen demand, nitrate, phosphate, total solids, turbidity and fecal coliforms), was collected at eleven sampling sites in the Sinos River Basin during 1991-1993. Five different ANN architectures, with one hidden layer and 2, 5, 10, 20 and 25 neurons were tested. The ANN models were trained using the gradient descendent …