Artificial intelligence techniques in flood forecasting
The need for reliable, easy to set up and operate, hydrological forecasting systems
is an appealing challenge to researchers working in the area of flood risk management.
Currently, advancements in computing technology have provided water
engineering with powerful tools in modelling hydrological processes, among them,
Artificial Neural Networks (ANN) and genetic algorithms (GA). These have been
applied in many case studies with different level of success. Despite the large amount
of work published in this field so far, it is still a challenge to use ANN models reliably
in a real-time operational situation. This thesis is set to explore new ways in improving
the accuracy and reliability of ANN in hydrological modelling. The study is
divided into four areas: signal preprocessing, integrated GA, schematic application
of weather radar data, and multiple input in flow routing.
In signal preprocessing, digital filters were adopted to process the raw rainfall
data before they are fed into ANN models. This novel technique demonstrated
that significant improvement in modelling could be achieved. A GA, besides finding
the best parameters of the ANN architecture, defined the moving average values for
previous rainfall and flow data used as one of the inputs to the model. A distributed
scheme was implemented to construct the model exploiting radar rainfall data. The
results from weather radar rainfall were not as good as the results from raingauge
estimations which were used for comparison. Multiple input has been carried out
modelling a river junction with excellent results and an extraction pump with results
not so promising.
Two conceptual models for flow routing modelling and a transfer function model
for rainfall-runoff modelling have been used to compare the ANN model's performance,
which was close to the estimations generated by the conceptual models and
better than the transfer function model.
The flood forecasting system implemented in East Anglia by the Environment
Agency, and the NERC HYREX project have been the main data sources to test