The decision making process in flood mitigation typically involves a number of factors reflecting flood severity, flood vulnerability and the cost of the mitigation measures, which implies that the decision framework needs to combine both social-economic parameters and flood extent prediction analysis. A socio-economic vulnerability index (SEVI) is developed here to represent social-economic factors and its use demonstrated within a multi-criteria decision analysis (MCDA) for assessing flood levee options for a central basin of Jakarta, Indonesia. The variables defining the SEVI are selected based on available national social-economic data reported for Indonesia with overlapping information removed using Pearson's correlation analysis. Two different methods are used to further scale the SEVI which is developed along administrative boundaries into a Net SEVI which is dependent on the predicted flood hazard as resulting from the levee plan options while capturing uncertainty in the rainfall forecasting. The MCDA technique adopted uses criteria of Net SEVI, annual expected loss, graduality and levee construction cost for analyzing six different levee plans and with uncertainty in the rainfall incorporated. The Net SEVI thus specifically reflects the social-economic impact on the flood-affected population, and this approach thereby provides a higher degree of granularity in the flood mitigation decision process. The MCDA decision framework developed is general in that the Net SEVI can be applied for consideration of other flood mitigation strategies. Here, it is shown that the inclusion of the Net SEVI criteria changes the best choice levee plan decision to a higher protection level for the basin considered.

[1] Epistemic uncertainty is a result of knowledge deficiency about the system. Sampling error exists when limited amounts of hydrologic data are used to estimate a T year event quantile. Both the natural randomness of hydrologic data and the sampling error in design quantile estimation contribute to the uncertainty in flood damage estimation. This paper presents a framework for evaluating a flood-damage-mitigation project in which both the hydrologic randomness and epistemic uncertainty due to sampling error are considered in flood damage estimation. Different risk-based decision-making criteria are used to evaluate project merits based on the mean, standard deviation, and probability distribution of the project net benefits. The results show that the uncertainty of the project net benefits is quite significant. Ignoring the data sampling error will underestimate the potential risk of each project. It can be clearly shown that adding data to existing sample observations leads to improved quality of information, enhanced reliability of the estimators, and reduced sampling error and uncertainty in the project net benefits. Through the proposed framework, the proper length of the extended record for risk reduction can be determined to achieve the required level of acceptable risk.

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