Two main approaches to enhance urban pluvial flood prediction were developed
and tested in this research: (1) short-term rainfall forecast based on rain gauge
networks, and (2) customisation of urban drainage models to improve hydraulic
simulation speed. Rain gauges and level gauges were installed in the Coimbra
(Portugal) and Redbridge (UK) catchment areas. The collected data was used
to test and validate the approaches developed.
When radar data is not available urban pluvial flooding forecasting can be based
on networks of rain gauges. Improvements were made in the Support Vector
Machine (SVM) technique to extrapolate rainfall time series. These improvements
are: enhancing SVM prediction using Singular Spectrum Analysis (SSA)
for pre-processing data; combining SSA and SVM with a statistical analysis that
gives stochastic results. A method that integrates the SVM and Cascade-based
downscaling techniques was also developed to carry out high-resolution (5-min)
precipitation forecasting with longer lead time. Tests carried out with historical
data showed that the new stochastic approach was useful for estimating the
level of confidence of the rainfall forecast. The integration of the cascade method
demonstrates the possibility of generating high-resolution rainfall forecasts with
longer lead time. Tests carried out with the collected data showed that water
level in sewers can be predicted: 30 minutes in advance (in Coimbra), and 45
minutes in advance (in Redbridge).
A method for simplifying 1D1D networks is presented that increases computational
speed while maintaining good accuracy. A new hybrid model concept was
developed which combines 1D1D and 1D2D approaches in the same model to
achieve a balance between runtime and accuracy. While the 1D2D model runs
in about 45 minutes in Redbridge, the 1D1D and the hybrid models both run
in less than 5 minutes, making this new model suitable for flood forecasting.