Extreme flooding is becoming a more serious hazard to the world's infrastructure, especially in high-risk locations, and is linked to global warming and human activity. This research employs an analytical hierarchy process (AHP) model and geographic information system (GIS) analysis to delineate flood risk zones. An eight-factor multiparametric method to flood risk susceptibility mapping includes precipitation, distance to river, the slope, elevation, land use/cover, topographic wetness index, type of soil, and curvature. An urban flood risk index (UFRI) is established based on vulnerability mapping, revealing that approximately 33% of Haripur District, Khyber Pakhtunkhwa, Pakistan, is prone to floods. Additionally, land use cover analysis indicates that 23% of the crop area in Haripur District is at risk from flood disasters. Recognizing the potential for costly damage to infrastructure, flood hazard mapping serves as a valuable tool to prioritize risk areas for urban and agricultural development. The outcomes of this study are anticipated to significantly contribute to predisaster flood control management in the studied area.

Flooding is one of the most severe natural hazards, causing widespread environmental, economic, and social disruption. If not managed properly, it can lead to human losses, property damage, and the destruction of livelihoods. The ability to rapidly assess such damages is crucial for emergency management. Near Real-Time (NRT) spatial information on flood-affected areas, obtained via remote sensing, is essential for disaster response, relief, urban and industrial reconstruction, insurance services, and damage assessment. Numerous flood mapping methods have been proposed, each with distinct strengths and limitations. Among the most widely used are machine learning algorithms and spectral indices, though these methods often face challenges, particularly in threshold selection for spectral indices and the sampling process for supervised classification. This study aims to develop an NRT flood mapping approach using supervised classification based on spectral features. The method automatically generates training samples through masks derived from spectral indices. More specifically, this study uses FWEI, NDVI, NDBI, and BSI indices to extract training samples for water/flood, vegetation, built-up areas, and soil, respectively. The Otsu thresholding technique is applied to create the spectral masks. Land cover classification is then performed using the Random Forest algorithm with the automatically generated training samples. The final flood map is obtained by subtracting the pre-flood water class from the post-flood image. The proposed method is implemented using optical satellite images from Sentinel-2, Landsat-8, and Landsat-9. The proposed method's accuracy is rigorously evaluated and compared with those obtained from spectral indices and machine learning techniques. The suggested approach achieves the highest overall accuracy (OA) of 90.57% and a Kappa Coefficient (KC) of 0.89, surpassing SVM (OA: 90.04%, KC: 0.88), Decision Trees (OA: 88.64%, KC: 0.87), and spectral indices like AWEI (OA: 84.12%, KC: 0.82), FWEI (OA: 88.23%, KC: 0.86), NDWI (OA: 85.78%, KC: 0.84), and MNDWI (OA: 87.67%, KC: 0.85). These results underscore the superior accuracy and effectiveness of the proposed approach for NRT flood detection and monitoring using multi-sensor optical imagery.