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Abstract Fluvial hazards of river mobility and flooding are often problematic for road infrastructure and need to be considered in the planning process. The extent of river and road infrastructure networks and their tendency to be close to each other creates a need to be able to identify the most dangerous areas quickly and cost‐effectively. In this study, we propose a novel methodology using random forest (RF) machine learning methods to provide easily interpretable fine‐scale fluvial hazard predictions for large river systems. The tools developed provide predictions for three models: presence of flooding (PFM), presence of mobility (PMM) and type of erosion model (TEM, lateral migration, or incision) at reference points every 100 m along the fluvial network of three watersheds within the province of Quebec, Canada. The RF models use variables focused on river conditions and hydrogeomorphological processes such as confinement, sinuosity, and upstream slope. Training/validation data included field observations, results from hydraulic and erosion models, government infrastructure databases, and hydro‐ geomorphological assessments using 1‐m DEM and satellite/historical imagery. A total of 1807 reference points were classified for flooding, 1542 for mobility, and 847 for the type of erosion out of the 11,452 reference points for the 1145 km of rivers included in the study. These were divided into training (75%) and validation (25%) datasets, with the training dataset used to train supervised RF models. The validation dataset indicated the models were capable of accurately predicting the potential for fluvial hazards to occur, with precision results for the three models ranging from 83% to 94% of points accurately predicted. The results of this study suggest that RF models are a cost‐effective tool to quickly evaluate the potential for fluvial hazards to occur at the watershed scale.
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Abstract High-resolution global flood risk maps are increasingly used to inform disaster risk planning and response, particularly in lower income countries with limited data or capacity. However, current approaches do not adequately account for spatial variation in social vulnerability, which is a key determinant of variation in outcomes for exposed populations. Here we integrate annual average exceedance probability estimates from a high-resolution fluvial flood model with gridded population and poverty data to create a global vulnerability-adjusted risk index for flooding (VARI Flood) at 90-meter resolution. The index provides estimates of relative risk within or between countries and changes how we understand the geography of risk by identifying ‘hotspots’ characterised by high population density and high levels of social vulnerability. This approach, which emphasises risks to human well-being, could be used as a complement to traditional population or asset-centred approaches.