Artificial neural networks (ANNs) have been used increasingly for modelling com-plex hydrological processes. In this paper, we present a self-organizing polynomial neural network (SOPNN) algorithm, which combines the theory of bio-cybernetic self-organizing polynomial (SOP) with the artificial neural network (ANN) approach. With the SOP feature of seeking the best combination of polynomial mode...

This paper shows that the simple point Voronoi diagram, together with the extraction of crust and skeleton information, can be of considerable value in a GIS environment, and that these form fundamental tools for the evaluation of spatial relationships. The “labelled skeleton” and the Voronoi skeleton are described, and are shown to be useful in the extraction of features from digitized or scan...

The SHETRAN physically-based distributed rainfall-runoff modelling system gives detailed simulations in time and space of water flow and sediment and solute transport in river catchments. It is therefore a powerful tool for studying hydrological and environmental impacts associated with land-use and climate change. A Graphical User Interface (GUI) has been developed that allows a catchment data...

Permeable Pavement (PP) models are valuable tools for studying the implementation of PPs in urban environments. However, runoff simulated by traditional such as Curve Number (CN) is different from that created with PP models, infiltration computed differently. many investigations compare both to extract broader conclusions without considering how two related. Hence, this research explores relat...