Floods have potential destructive effects on socioeconomic facilities and cause serious risks for people. During the last decades lots of efforts have been carried out 10 overcome the difficulties caused by this natural phenomenon. In the past, most of the studies have been focused on developing mathematical models to forecast flood events in real -time to provide precautionary activities. The models are various from simple structures to models with high complexity and according to the climate conditions of the catchment under study, most appropriate model must be selected to predict flood events by using the existing recorded data from the catchment Rainfall-runoff model is the main component of a real-time flood forecasting model and transforms rainfall to runoff. The model commonly consists of a number of mathematical equations and parameters which are interconnected together for simulating runoff over a catchment. Since a model is a simplification of the real hydrological system, errors In simulation are unavoidable and influence on the simulation accuracy. Hence. the model should be selected properly and requires to be updated continuously to cope with probable hydrological changes which could create errors on model simulations. The current research focus on real-time flood forecasting by improving and developing rainfall-runoff models and indicating solutions to update the model to cope with frequent hydrological changes which can reduce the model performance. The research was started by evaluating optimisation schemes to derive the model parameters and an optimisation method was proposed based on Genetic algorithm concept. On the second stage, a new rainfall -runoff model called ERM, was introduced and suggested as a reliable model to use In rainfall -runoff modeling. Moreover, the adaptability of the ERM model parameters to cope with different errors occurred in terms of modeling was considered. Finally, in the last part of the thesis, the ERM model was coupled with a well-known numerical filter called the Kalman Filter and a real-time flood forecasting model was introduced.
| Identifer | oai:union.ndltd.org:bl.uk/oai:ethos.bl.uk:617587 |
| Date | January 2013 |
| Creators | Baymani-Nezhad, Matin |
| Publisher | University of Bristol |
| Source Sets | Ethos UK |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
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