Abstract
The appraisal of appropriate levels of investment for devising flooding mitigation and to support recovery interventions is a complex and challenging task. Evaluation must account for social, political, environmental and other conditions, such as flood state expectations and local priorities. The evaluation method should be able to quickly identify evolving investment needs as the incidence and magnitude of flood events continue to grow. Quantification is essential and must consider multiple direct and indirect effects on flood related outcomes. The method proposed is this study is a Bayesian network, which may be used ex-post for evaluation, but also ex-ante for future assessment, and near real-time for the reallocation of investment into interventions. The particular case we study is the effect of flood interventions upon mental health, which is a gap in current investment analyses. Natural events such as floods expose people to negative mental health disorders including anxiety, distress and post-traumatic stress disorder. Such outcomes can be mitigated or exacerbated not only by state funded interventions, but by individual and community skills and experience. Success is also dampened when vulnerable and previously exposed victims are affected. Current measures evaluate solely the effectiveness of interventions to reduce physical damage to people and assets. This paper contributes a design for a Bayesian network that exposes causal pathways and conditional probabilities between interventions and mental health outcomes as well as providing a tool that can readily indicate the level of investment needed in alternative interventions based on desired mental health outcomes.
Highlights
Natural hazards can have large societal impacts
We evaluate the impact of the flood interventions on the mental health of people affected by flooding using a Bayesian network (BN) trained by a combination of the data extracted from a narrative in the relevant literature from the published reports and expert judgments
We first need to learn the BN for a subset of the risk factors selected in relation to the flood intervention’s impact upon mental health, including the prevalence of probable depression in people who have been flooded (Flood), loss of sentimental items (LSOI), prevalence of loss of sentimental item as secondary stressor in those exposed to flooding (PPD), less severe depression (Lsever), and more severe depression (Msever)
Summary
Natural hazards can have large societal impacts. It is estimated that they caused. Flood risk is defined in the European Flood Directive as “the combination of the probability of a flood event and of the potential adverse impacts on human health, the environment, cultural heritage and economic activity associated with a flood event” [11]. The evaluation method must be able to swiftly determine changed investment requirements as the incidence and magnitude of flood events continue to grow This quantification is essential and must examine various direct and indirect flood impacts on flood related outcomes in a probabilistic manner. In order to reduce the damage to the community and people caused by flood events, environmental agencies are using various interventions each with different outcomes, efficiencies and costs. This study provides an efficient construction of a probabilistic BN that displays causal pathways and their probabilities between interventions and mental health outcomes, as well as providing a tool that can readily indicate the level of investment needed for alternative interventions based on anticipated mental health outcomes
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