## 1 Introduction  
Flood hazards are primary hazards associated with exposure to water [1]. Flooding areas have water logging or soil erosion [2]. Storms and climate change increase flood hazards [3]. Consequencesof flooding are often severe and directly impact human life by the interruption of services and the destruction of infrastructure and health as a result of famine and disease [2]. Worldwide, flooding is currently responsible for economic loss: it is one of the most expensive natural hazards in the Americas, Europe, and Asia [4; 5; 6; 7]. Mitigating the hazardous effects and managing comprehensive flood risks has become increasingly imperative [5; 6; 7; 8]. Combining spatial analysis and mathematical models to assess flood hazards is internationally recognized. A geographical information system (GIS) has been used to map the flood-prone regions and to establish accurate flood hazard maps [8]. However, even when GIS displays the submerged regions, the latent impacts and origins of the floods are missing. A combination of GIS with the flood fatality hazard (FFH) or the flood damage hazard (FDH) provides flood characteristics on mortality caused by flood hazards [9]. An ordered weighted averaging-based GIS analysis has been used to assess flooding areas and to develop flood hazard maps [10]. A hybrid approach with a combination of GIS, remote sensing, and spatial Monte Carlo Analysis has been developed to calculate flood damage and loss of life risks [11]. The hydraulic-GIS combined model has been used to map flood hazards in urban areas [12]. An integrated approach using Hydrological Engineering Centre (HEC) models, GIS and remote sensing has been developed to generate maps of flood plain and flood hazard [13]. A commercial relational database management system has been linked with a GIS-based decision support system to analyze flood risks. Multiple-criteria decision-making (MCDM) has allowed defining optimal specifications for assessing the factors influencing the likelihood of floods [14]. The analytic hierarchy process (AHP) combined with GIS has been used to evaluate the flood hazard with natural and anthropogenic factors [15], and to map flood risks [16; 17; 18; 19]. A fuzzy analytic hierarchy process (AHP) has assessed flood risk comprehensively [20]. The fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) allowed for evaluating and ranking the substantial levels of associated natural disaster risks [21; 22; 23]. Each method has its own advantage of evaluating impact factors of the floods. The outcome has been questioned due to its robustness because the experts' consultancies require to be weighted to provide more reliable results. Integrating Delphi fuzzy AHP-TOPSIS with GIS allowed for addressing the uncertainty of the weighting criteria and assessing the flood vulnerability with stream-based analysis [20]. The Delphi technique initially selected proper assessment criteria for a particular area, and GIS was used to show the spatial outcome of all criteria [23]. Fuzzy AHP-TOPSIS originally used fuzzy AHP for resolving the associate weights and used fuzzy TOPSIS for ranking the alternatives [24]. This study develops a hybrid approach using a combination of fuzzy AHP, fuzzy TOPSIS, and GIS to assess flood hazards along the South-Central Coast of Vietnam based on both natural and anthropogenic factors. The region covers eight coastal provinces (Da Nang, Quang Nam, Quang Ngai, Binh Dinh, Phu Yen, Khanh Hoa, Ninh Thuan, and Binh Thuan), with a total area of 44.54 thousand square kilometers, and a population of 9.4 million people. The area hosts most important coastal cities including Danang (the city under the central government, acting as the economic center of the region), Hoi An (the city with the UNESCO World Heritage Old Quarter), and Nha Trang and Phan Thiet (prominent tourist destinations with many resorts). The South-Central Coast is affected by tropical storms, floods, droughts, and saline intrusions.