WIT Press


Agent-based Modelling And Inundation Prediction To Enable The Identification Of Businesses Affected By Flooding

Price

Free (open access)

Paper DOI

10.2495/FRIAR140021

Volume

184

Pages

10

Page Range

13 - 22

Published

2014

Size

2822 kb

Author(s)

G. Coates, G. I. Hawe, N. G. Wright & S. Ahilan

Abstract

Flooding continues to cause significant disruption to individuals, organisations and communities in many parts of the world. In terms of the impact on businesses in the United Kingdom (UK), flooding is responsible for the loss of millions of pounds to the economy. As part of a UK Engineering and Physical Sciences Research Council funded project on flood risk management, SESAME, research is being carried out with the aim of improving business response to and preparedness for flood events. To achieve this aim, one strand of the research is focused on establishing how agent-based modelling and simulation can be used to evaluate and improve business continuity. This paper reports on the development of the virtual geographic environment (VGE) component of an agent-based model and how this has been combined with inundation prediction to enable the identification of businesses affected by flooding in any urban area of the UK. The VGE has been developed to use layers from Ordnance Survey’s MasterMap, namely the Topography Layer, Integrated Transport Network Layer and Address Layer 2. Coupling the VGE with inundation prediction provides credibility in modelling flood events in any area of the UK. An initial case study is presented focusing on the Lower Don Valley region of Sheffield leading to the identification of businesses impacted by flooding based on a predicted inundation. Further work will focus on the development of agents to model and simulate businesses during and in the aftermath of flood events such that changes in their behaviours can be investigated leading to improved operational response and business continuity.

Keywords

floods, businesses, agent-based modelling and inundation prediction.