Throughout the world, the likelihood of floods and managing the associated risk are a
concern to many catchment managers and the population residing in those catchments …

The quantification of urban impervious area has important implications for the design and
management of urban water and environmental infrastructure systems. This study proposes …

With urbanization is on the rise, it wreaks havoc on the hydrological processes within a
catchment, resulting in a deteriorating water environment. Floods can greatly raise the cost …

Abstract Convolutional Neural Networks (CNN) consist of various hyper-parameters which
need to be specified or can be altered when defining a deep learning architecture. There are …

Urban flooding is escalating worldwide due to the increasing impervious surfaces from
urban developments and frequency of extreme rainfall events by climate change. Traditional …

Flood detention basin (FDB) is an important part of the flood control system in the basin. It is
of great significance for scientific flood control to obtain the land use and land cover (LULC) …

In response to challenges in land cover classification (LCC), many researchers have
experimented recently with classification methods based on artificial intelligence techniques …

Urban impervious surfaces, a symbol of urbanisation, have permanently changed urban
hydrology behaviour and play a critical role in modelling rainfall-runoff process. The …

Our aim is to propose a new deep learning framework approach which uses an ensemble of
convolutional neural network (CNN) for land use-land cover mapping. Every CNN layer was …

Given the high financial and institutional cost of collecting and processing accurate
topography data, many large-scale flood hazard assessments continue to rely instead on …