A knowledge-based approach to modelling fast response catchments

Wedgwood, O 1993, A knowledge-based approach to modelling fast response catchments , PhD thesis, University of Salford.

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This thesis describes research in to flood forecasting on rapid response catchments, using knowledge based principles. Extensive use was made of high resolution single site radar data from the radar site at Hameldon Hill in North West England. Actual storm events and synthetic precipitation data were used in an attempt to identify 'knowledge' of the rainfall - runoff process. Modelling was carried out with the use of transfer functions, and an analysis is presented of the problems in using this type of model in hydrological forecasting. A physically realisable' transfer function model is outlined, and storm characteristics were analysed to establish information about model tuning. The knowledge gained was built into a knowledge based system (KBS) to enable real-time optimisation of model parameters. A rainfall movement forecasting program was used to provide input to the system. Forecasts using the KBS tuned parameters proved better than those from a naive transfer function model in most cases. In order to further improve flow forecasts a simple catchment wetness procedure was developed and included in the system, based on antecedent precipitation index, using radar rainfall input. A new method of intensity - duration - frequency analysis was developed using distributed radar data at a 2Km by 2Km resolution. This allowed a new application of return periods in real time, in assessing storm severity as it occurs. A catchment transposition procedure was developed allowing subjective catchment placement infront of an approaching event, to assess rainfall `risk', in terms of catchment history, before the event reaches it. A knowledge based approach, to work in real time, was found to be successful. The main drawback is the initial procurement of knowledge, or information about thresholds, linkages and relationships.

Item Type: Thesis (PhD)
Schools: Schools > School of Computing, Science and Engineering
Funders: Science and Engineering Research Council
Depositing User: USIR Admin
Date Deposited: 13 Jul 2017 10:26
Last Modified: 13 Oct 2021 13:23
URI: http://usir.salford.ac.uk/id/eprint/42961

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