Effects of Different Normalization, Aggregation, and Classification Methods on the Construction of Flood Vulnerability Indexes

Abstract

Index-based approaches are widely employed for measuring flood vulnerability. Nevertheless, the uncertainties in the index construction are rarely considered. Here, we conducted a sensitivity analysis of a flood vulnerability index in the Maquiné Basin, Southern Brazil, considering distinct normalization, aggregation, classification methods, and their effects on the model outputs. The robustness of the results was investigated by considering Spearman’s correlations, the shift in the vulnerability rank, and spatial analysis of different normalization techniques (min-max, z-scores, distance to target, and raking) and aggregation methods (linear and geometric). The final outputs were classified into vulnerability classes using natural breaks, equal interval, quantiles, and standard deviation methods. The performance of each classification method was evaluated by spatial analysis and the Akaike’s information criterion (AIC). The results presented low sensitivity regarding the normalization step. Conversely, the geometric aggregation method produced substantial differences on the spatial vulnerability and tended to underestimate the vulnerability where indicators with low values compensated for high values. Additionally, the classification of the vulnerability into different classes led to overly sensitive outputs. Thus, given the AIC performance, the natural breaks method was most suitable. The obtained results can support decision-makers in reducing uncertainty and increasing the quality of flood vulnerability assessments.

Review article: A systematic review and future prospects of flood vulnerability indices

“…With respect to the sensitivity analysis (SA), only nine papers (9.5 %) performed it. Most articles applied local SA by comparing the results obtained by changing input methods, such as choosing different weights (Müller et al, 2011;Nazeer and Bork, 2019;Rogelis et al, 2016), aggregation methods (Fernandez et al, 2016;Nazeer and Bork, 2019), or indicators (Rogelis et al, 2016;Zhang and You, 2014). In addition, Abebe et al (2018) quantified sensitivity through variance reduction and mutual information.…”

Integrated flood vulnerability assessment of villages in the Waimanu River Catchment in the South Pacific: the case of Viti Levu, Fiji

“…Flood vulnerability index is one of the most effective methods of flood vulnerability assessment especially in holistic studies to enable a better representation of reality as it encompasses people's ability to cope, adapt, and respond to hazards in evaluating the potential impacts of flooding (Balica et al 2013;Nasiri et al 2016;de Brito et al 2018;Moreira et al 2021aMoreira et al , 2021b. It is a parametric approach which aims to build a picture of the vulnerability of an area consequently, assessing the flooding risk of that area by incorporating the four components of flood vulnerability including social, economic, physical, and environmental and…”

“…The FVI summarizes the complex and multidimensional issues to assist stakeholders in interpreting a phenomenon and making decisions on longterm flood management and prioritizing adaptation (Balica 2012). To avoid misinterpretation, a clear demonstration of the weighting, normalization, and aggregation methods used to construct the index is mandatory (Moreira et al 2021b). Some studies which have used the FVI comprise Komi et al (2016) in Nigeria, Karmaoui and Balica (2019) in Morocco, Nazeer and Bork (2019) in Pakistan, Nguyen and Van Nguyen (2019) in Vietnam, and Halavatau et al (2020) in PICs.…”

“…In consideration of the above, thirty-five interacting indicators in accordance with the CIVA narratives were used to determine the adaptive capacity (Table A3 in Appendix A of the Supplementary Materials) of the communities. A subjective scoring system based on a Likert scale adapted and modified from Weis et al (2016), the CIVA framework (Dumaru and PaCE-SD 2017), and from the normalization method employed by Moreira et al (2021b), was used to compile the scores of the 46 indicators for each community. These scores were used to compute the mean indices of the three risk factors for every community.…”

Determination of the LNG Bunkering Optimization Method for Ports Based on Geometric Aggregation Score Calculation

“…Mou et al [16] classified various factors for the quantitative evaluation of the port potential in the Yangtze River Delta and evaluated various ports using the FAHP (Fuzzy Analytical Hierarchy Process) entropy model. Moreira et al [17] emphasized that index-based approaches are widely employed for measuring flood vulnerability; however, uncertainties in index construction are rarely considered. Surisa et al [18] presented a general overview of selected aggression instruments and assisted in identifying instruments that may be best-suited.…”