Flood hazards resulting from short-term severe precipitation have caused serious social and economic losses and have posed extraordinary threats to the safety of lives and property. Vulnerability, which reflects the degree of the adverse impact of flooding on a city, the sensitivity of the environment, and the extent to which rescues are possible during flooding, is one of the significant factors of the disaster risk assessment. Because of this, this paper proposes an Environmental Vulnerability Analysis Model (EVAM), based on comprehensively evaluating multi-source remote sensing data. The EVAM includes a two-stage, short-term flood vulnerability assessment. In the first stage, the flood’s areal extension and land-use classification are extracted, based on the U-NET++ network, using multi-source satellite remote sensing images. The results from the first stage are used in the second stage of vulnerability assessment. In the second stage, combining multi-source data with associated feature extraction results establishes the Exposure–Sensitivity–Adaptive capacity framework. The short-term flood vulnerability index is leveraged through the analytic hierarchy process (AHP) and the entropy method is calculated for an environmental vulnerability evaluation. This novel proposed framework for short-term flood vulnerability evaluation is demonstrated for the Henan Province. The experimental results show that the proportion of vulnerable cities in the Henan Province ranging from high to low is 22.22%, 22.22%, 38.89%, and 16.67%, respectively. The relevant conclusions can provide a scientific basis for regional flood control and risk management as well as corresponding data support for post-disaster reconstruction in disaster regions.

Flood hazards caused by intense short-term precipitation have led to significant social and economic losses and pose serious threats to human life and property. Accurate disaster risk assessment plays a critical role in verifying disaster statistics and supporting disaster recovery and reconstruction processes. In this study, a novel Large-Scale Flood Risk Assessment Model (LS-FRAM) is proposed, incorporating the dimensions of hazard, exposure, vulnerability, and coping capacity. Multi-source heterogeneous data are utilized for evaluating the flood risks. Soil erosion modeling is incorporated into the assessment framework to better understand the interactions between flood intensity and land surface degradation. An index system comprising 12 secondary indicators is constructed and screened using Pearson correlation analysis to minimize redundancy. Subsequently, the Analytic Hierarchy Process (AHP) is utilized to determine the weights of the primary-level indicators, while the entropy weight method, Fuzzy Analytic Hierarchy Process (FAHP), and an integrated weighting approach are combined to calculate the weights of the secondary-level indicators. This model addresses the complexity of large-scale flood risk assessment and management by incorporating multiple perspectives and leveraging diverse data sources. The experimental results demonstrate that the flood risk assessment model, utilizing multi-source data, achieves an overall accuracy of 88.49%. Specifically, the proportions of areas classified as high and very high flood risk are 54.11% in Henan, 31.74% in Shaanxi, and 18.2% in Shanxi. These results provide valuable scientific support for enhancing flood control, disaster relief capabilities, and risk management in the middle and lower reaches of the Yellow River. Furthermore, they can furnish the necessary data support for post-disaster reconstruction efforts in impacted areas.