Flood Susceptibility Assessment Using Geospatial Techniques

Flood susceptibility assessment employs a suite of spatial data and analytical methods to predict where inundation is most likely to occur. By integrating topographic, hydrological, meteorological and land-cover information within a Geographic Information System (GIS), researchers generate susceptibility maps that guide planning and early-warning systems. Remote sensing platforms supply regular updates on surface conditions, while multi-criteria decision analysis and statistical models weight the influence of factors such as elevation, slope, drainage density and soil type. More recently, machine-learning algorithms have been introduced to capture non-linear interactions among conditioning variables, often improving predictive accuracy. Susceptibility studies inform infrastructure design, flood-plain zoning and ecosystem restoration, and are increasingly coupled with real-time monitoring for dynamic risk management. Across urban and rural environments, these assessments address the growing challenge of extreme rainfall events and sea-level rise by providing a data-driven basis for resilience strategies. Practical applications range from delineating priority areas for flood defences to optimising land-use policies. The global scale of flood hazards demands transferable methodologies, yet assessments must be tailored to local climatic, geomorphological and socio-economic contexts. As geospatial datasets proliferate, the field continues to evolve towards higher resolution mapping, automated data processing and integration with participatory platforms for community-centred decision making.

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Urban flood mapping in Chengdu city has harnessed social media reports to augment traditional archives of flood events. By treating crowd-sourced posts as response data and selecting ten physiographic and infrastructural predictors, a Naïve Bayes model was trained within a GIS environment. Validation via the Receiver Operating Characteristic curve yielded an area under the curve of 0.83, demonstrating reliable spatial predictions and offering a novel source of real-time information for municipal early-warning systems. In the Bagmati River basin, Nepal, a comparative study applied both the expert-driven Analytical Hierarchy Process and the data-driven Frequency Ratio model to generate flood-vulnerability maps. The Frequency Ratio approach achieved 91% accuracy in validation, outperforming the AHP’s 84% and underscoring the value of empirical weighting of conditioning factors such as precipitation, drainage density and land-cover. A separate investigation employed Sentinel-1 radar imagery in conjunction with ensemble machine learning, using a bagging meta-classifier based on cubic K-Nearest Neighbour models. Ten remotely sensed and topographic variables were integrated to produce flood-susceptibility forecasts, with the ensemble reducing overfitting and improving prediction stability. These diverse approaches illustrate the shift towards hybrid datasets and advanced algorithms to enhance spatial predictivity and operational relevance.

Flood Susceptibility Assessment Using Geospatial Techniques publication trend

The graph below shows the total number of articles in flood susceptibility assessment using geospatial techniques across all publications each year (not limited to Nature Index journals).

Technical terms

Geographic Information System (GIS): A computer-based framework for capturing, storing, analysing and visualising spatial data layers.

Remote Sensing: The acquisition of information about Earth’s surface via satellite or airborne sensors, used to monitor hydrological and land-cover changes.

Susceptibility Assessment: The process of evaluating the likelihood of flooding in a given area based on multiple conditioning factors.

Analytical Hierarchy Process (AHP): A structured decision-making technique that derives weights for criteria through expert judgements.

Frequency Ratio (FR): A statistical method that calculates the probability of flood occurrence by relating past events to specific factor classes.

Naïve Bayes Classifier: A probabilistic machine-learning algorithm that predicts class membership based on Bayes’ theorem with strong (naïve) independence assumptions.

Ensemble Machine Learning: A modelling approach that combines multiple base learners to improve predictive performance and reduce overfitting.

Receiver Operating Characteristic (ROC) Curve: A plot of true positive rate against false positive rate used to assess the discrimination ability of predictive models, summarised by the area under the curve.

References

1. Urban flood susceptibility mapping based on social media data in Chengdu city, China. Sustainable Cities and Society (2023).
2. Flood vulnerability map of the Bagmati River basin, Nepal: a comparative approach of the analytical hierarchy process and frequency ratio model. Smart Construction and Sustainable Cities (2024).
3. Flood Detection and Susceptibility Mapping Using Sentinel-1 Remote Sensing Data and a Machine Learning Approach: Hybrid Intelligence of Bagging Ensemble Based on K-Nearest Neighbor Classifier. Remote Sensing (2020).