Title:
MODELLING A COMMUNITY RESILIENCE INDEX FOR URBAN FLOOD-PRONE AREAS
Abstract:
Urban Resilience for any city is a gap to be fully understood and assimilated in urban planning.
Globalization and rapid urbanization in recent years have led to newer challenges as higher densities, greater demand for infrastructure, resources, and ‘environmental and man-made hazards’
that are on the rise. Cities try to cope with the rising demands through various planning techniques and modern applications and planning models. However, the landscape of each city is different and many times these approaches might not be the best fit for every aspect of a city. One of the worst situations where this reality would be revealed is the resilience shown by a city when disaster strikes. The flood in Kerala State that occurred in August 2018 is an example where resilience can be studied by examining how people and institutions cope with the maximum damage, and how quickly lives can be brought back to normal. Determining what the ‘original condition was before the hazard,’ or the ‘higher or acceptable lowest’ levels, are among the first questions to be resolved.
In most cases, governmental, private, and non-governmental agencies strive to attain the ‘lowest acceptable level.’ However, it is most appreciated if the achieved resilience level exceeds the original level by adopting a better approach to planning, design, and infrastructural capabilities.
The ability and the extent of resilience shown by Kerala are very good as compared to similar scale disasters that have struck other locations in India. The thesis creates a Community Resilience Index for urban Flood-prone areas (CRIF) through a rigorous bottom-up approach with reference to Kerala. Criteria for the proposed index were developed using multi-criteria decision analysis that began with a focus-group discussion, a literature study, fuzzy Delphi analysis, empirical study using multi-variate Probit regression, and AHP analysis. The index is designed according to the Sendai Framework for Disaster Risk Reduction (2015-2030). The CRIF Index is put to test through a case study. Further, the results are also validated using three different techniques. The thesis ends with formulation of appropriate policies for future flood-resilient urban planning.
Keywords: community resilience, index, flood, analytic hierarchy process, fuzzy Delphi, Probit regression
