Search
Now showing items 1-6 of 6
Machine learning models for stream-level predictions using readings from satellite and ground gauging stations
(2024-04-19)
While the accuracy of flood predictions is likely to improve with increasing gauging station networks and robust radar coverage, challenges arise when such sources are spatially limited [1]. For instance, severe rainfall ...
Comprehensive Flood Early Warning Systems: From Modelling to Policy Making Perspectives
(2024-04-19)
Todays, early warning systems are widely applied in real-time flood forecasting operations as valuable non-structural tools for mitigating the impacts of floods [1]. Although many research works have perfectly could review ...
Optimising oceanic rainfall estimates for increased lead time of stream level forecasting: A case study of GPM IMERG estimates application in the UK
(2024-04-19)
Among the three main rainfall data sources (rain gauge stations, rainfall radar stations and weather satellites), satellites are often the most appropriate for longer lead times in real-time flood forecasting [1]. This is ...
Stakeholder analysis in the application of cutting-edge digital visualisation technologies for urban flood risk management: A critical review
(2024-02-02)
Cutting-edge flood visualisation technologies are becoming increasingly important in managing urban flood risks, particularly from the perspective of stakeholders who play a crucial role in controlling and reducing the ...
A critical review of digital technology innovations for early warning of water-related disease outbreaks associated with climatic hazards
(2024-01-01)
Water-related climatic disasters pose a significant threat to human health due to the potential of disease outbreaks, which are exacerbated by climate change. Therefore, it is crucial to predict their occurrence with ...
Enhancing Urban Flood Forecasting in Drainage Systems Using Dynamic Ensemble-based Data Mining
(2023-12-01)
This study presents a novel approach for urban flood forecasting in drainage systems using a dynamic ensemble-based data mining model which has yet to be utilised properly in this context. The proposed method incorporates ...