Decisions Under Deep Uncertainty: A Pythagorean Fuzzy Z-Number Framework for Urban Flood Risk Assessment

Abstract

Ambiguities are always present in real-world decision-making situations; in the case of floodplain urban growth, the planning process is further complicated by environmental unpredictability, limited data, and experts' subjective opinions. The current study proposes an enhanced multi-attribute decision-making (MADM) framework that incorporates Hamacher aggregation operations and Pythagorean fuzzy Z-numbers (PFZNS) to address the aforementioned issues. The proposed approach recommends constructing and investigating Hamacher geometric aggregation operators for decision-making problems involving multiple criteria. A variety of options are considered depending on population density, infrastructure cost, land use suitability, flood risk, and environmental effects to evaluate flood risk in urbanized floodplains in Bangladesh using the model. The suggested method is implemented and tested using a case study in an urban floodplain in Bangladesh. The results show that the method yields consistent, stable rankings of alternatives, accounts for uncertainty in experts' opinions and flood information, and improves decision-making quality. In conclusion, the PFZN-MADM model presented in this study is reliable and flexible, enabling more confident decision-making in flood-prone areas to encourage sustainable city development.