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Hydrographical Flow Modelling of the River Severn Using Particle Swarm Optimization

Kenny, Ian (2019). Hydrographical Flow Modelling of the River Severn Using Particle Swarm Optimization. The Computer Journal (Early Access).

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DOI (Digital Object Identifier) Link: https://doi.org/10.1093/comjnl/bxz106
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Abstract

A model is presented to model hydrographical flow, which we apply to flood forecasting in the River Severn catchment area. The approach uses Particle Swarm Optimization (PSO), a swarm computation heuristic, to produce a predictive model of hydrographical flow. Hydrological flow data from 1980 to 1990 are considered, comprising the daily average flow through the River Severn and its tributaries. PSO models are developed from each year of data and are applied to predict flow in the other 10 years; model performance is shown to be largely independent of the training year, suggesting the catchment system is stable and the approach is robust. Importantly, and in contrast to most of the existing alternatives, flow is derived from data measurements taken 2 days previously, as demanded for early-warning flood prediction. The cross-validated model for prediction of extreme (Q95) events R2 = 0.96, significantly improving upon multiple linear regression R2 = 0.93, the best performing of current existing methods.

Item Type: Journal Item
Copyright Holders: 2019 The British Computer Society
ISSN: 0010-4620
Keywords: particle swarm; optimization; River Severn; hydrographical flow; prediction; machine learning
Academic Unit/School: Faculty of Science, Technology, Engineering and Mathematics (STEM) > Computing and Communications
Faculty of Science, Technology, Engineering and Mathematics (STEM)
Research Group: Centre for Research in Computing (CRC)
Item ID: 67049
Depositing User: Ian Kenny
Date Deposited: 20 Nov 2019 09:33
Last Modified: 24 Jan 2020 16:36
URI: http://oro.open.ac.uk/id/eprint/67049
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