How the past influences the future: flood risk perception in informal settlements

ABSTRACT

This study presents fresh evidence from an informal settlement in Accra, Ghana, examining how knowledge, understanding, experiences, and feelings about flood risk influence the flood risk perceptions of residents. The study adopted a mixed-methods approach, involving the collection and analysis of qualitative and quantitative data. We collected the data through seventeen interviews and 392 household surveys in Glefe, Accra, Ghana. We then conducted a thematic analysis of the qualitative data to understand participants' perceptions and the factors influencing their flood risk perceptions. The factors were used to produce hypotheses about flood risk perception. We subsequently performed regression analyses using the quantitative data to test the hypothesised relationships. The findings revealed that fear, flood experience, and coping experience were the major factors influencing residents' flood risk perceptions. Taken together, these factors had varying levels of influence on risk perceptions, with fear being the most statistically significant. However, it seems that experience held sway over residents' opinions, views, and perceptions. The perceived likelihood of future flooding events was therefore determined by residents' experience with flooding and coping. The study recommends incorporating the flooding and coping experiences of residents into adaptation mechanisms because these influence their perceptions of the flooding risks.

KEYWORDS:

• Flood risk management
• coping experience
• adaptation
• protection motivation
• informal settlements

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 Based on 2010 population and housing census, Glefe was inhabited by 8,738 people (approximately 2,368 households and 1074 houses). The sample is representative of this population. Households in 36% of the houses participated in the survey.

2 The variable (FRA) was obtained by using WarpPLS to generate factor scores for each of the latent variables. To do this, we first processed the raw data using WarpPLS, generating standardized indicators. We then conducted structural equation modelling analysis of the relationship between PS and PV, using their indicators. Factor scores of PS and PV were generated through the analysis and added to the dataset as new standardized indicators, converting PS and PV from latent variables to indicators. Based on literature, PS and PV were then used as indicators of FRA. For more information on this process, see Kock (2020).

3 A negative and statistically significant correlation was evident in the simple linear correlation. However, the multiple linear correlation was not statistically significant.