Climate change and extreme weather events are exacerbating flood risks across vulnerable regions worldwide. Robust flood hazard modelling is crucial to support disaster resilience and adaptation efforts. This article explores how the integration of advanced machine learning techniques and multi-sourced geospatial data can enhance the accuracy and granularity of flood damage assessments.
Flood Hazard Mapping
Conventional flood mapping approaches often struggle to capture the full complexity of flood dynamics, relying on scarce in-situ data, overlooking interactions between causal factors, and failing to represent nonlinear processes. The emergence of geospatial technologies and computational capabilities has enabled a paradigm shift towards data-driven flood modelling frameworks.
Recent studies have demonstrated the value of integrating diverse spatial datasets within GIS-based modelling to delineate flood hazard zones. Factors such as terrain, land use/cover, soil type, drainage density, rainfall distribution, and proximity to river networks can significantly influence flood susceptibility and need to be considered. Multi-criteria analysis enables the weighting and aggregation of these heterogeneous datasets to identify high-risk areas.
Remote sensing data provides additional insights by offering up-to-date, high-resolution information on land use dynamics and hydrological variables that shape flood exposure. The synergistic integration of GIS, multi-criteria evaluation (MCE), and the analytical hierarchy process (AHP) has shown promising results in regional and national-scale flood hazard mapping, particularly for data-scarce regions.
While methods like random forest, artificial neural networks, support vector machines, gradient-boosted decision trees, and others have enabled some predictive flood modelling, there is ample room for improvement and innovation in techniques that leverage spatial big data. The selection of flood conditioning factors varies across studies due to differences in data availability, study scale, and modelling approaches. However, key factors such as topography, land use, soil, rainfall, proximity, and demography are widely applied in GIS-based flood hazard assessment.
Flood Damage Assessment
Accurate mapping of flood hazards is crucial for effective disaster risk reduction and flood management. However, traditional approaches often fail to represent the full complexity of flood dynamics, leading to suboptimal risk assessments. The integration of Google Earth Engine Cloud with GIS-RS-ML frameworks offers advanced, dynamic tools capable of providing diverse data for risk management, flood zoning, and forecasting.
Given that natural disasters are multidimensional phenomena with a strong spatial component, GIS-ML techniques are particularly well-suited for this type of analysis as they can handle large volumes of spatial data used in flood modelling. Flood risk management strategies rely heavily on modelling the hydrological, meteorological, and topographic factors of a catchment area to mitigate flood risks in real-time. Prioritizing risk analyses and utilizing these innovative frameworks is essential for timely completion.
Machine Learning for Flood Modelling
The study focused on the flood-prone Arambag region in Hooghly district, India, which has a history of recurring flood events and remains highly susceptible due to its geomorphic settings and climatic influences. The research aimed to develop an interpretable and validated machine learning framework for flood hazard categorization in this vulnerable area.
An inventory of historical and event-based flood extent was created using Sentinel-1 SAR data analysis and global flood databases. Fifteen flood conditioning factors encompassing topography, land cover, soil type, precipitation, and anthropogenic variables were assembled. Rigorous training and testing of state-of-the-art machine learning models, including random forest, AdaBoost, rFerns, XGB, DeepBoost, GBM, SDA, BAM, monmlp, and MARS algorithms, were undertaken for categorical flood hazard mapping.
Model optimization was achieved through statistical feature selection techniques. Accuracy metrics and advanced model interpretability methods like SHAP and Boruta were implemented to evaluate predictive performance. According to the area under the receiver operating characteristic curve (AUC), the prediction accuracy of the models performed was around > 80%. Random forest achieves an AUC of 0.847 at resampling factor 5, indicating strong discriminative performance. AdaBoost also consistently exhibits good discriminative ability, with AUC values of 0.839 at resampling factor 10.
Boruta and SHAP analysis indicated precipitation and elevation as factors most significantly contributing to flood hazard assessment in the study area. Most of the machine learning models pointed out southern portions of the study area as highly susceptible to flooding. On average, from 17.2 to 18.6% of the study area is predicted to be at very high flood risk.
Geospatial Data Integration
The study’s robust integration of multi-sourced spatial datasets, including Sentinel-1 SAR imagery, global flood databases, and in-situ surveys, enabled a comprehensive characterization of the region’s flood patterns. The incorporation of 15 flood conditioning factors, encompassing terrain, land cover, soil, rainfall, proximity, and demographic variables, ensured a holistic representation of the factors influencing flood susceptibility.
Rigorous validation, using various statistical performance metrics and cross-validation techniques, added credibility to the results. The application of state-of-the-art machine learning algorithms, including random forest, AdaBoost, rFerns, XGB, DeepBoost, GBM, SDA, BAM, monmlp, and MARS, demonstrated their enhanced modelling capabilities compared to conventional approaches.
To refine the input variables, the study employed a combination of techniques, such as OLS regression, multicollinearity analysis, and nature-inspired algorithms. This optimization process identified precipitation, geomorphology, elevation, lithology, and topographic wetness index as the most significant factors for flood hazard assessment in the Arambag region.
Flood Risk Assessment and Mitigation
The research findings provide valuable insights into the potential impacts of flood hazards on buildings and cropland in the study area. The majority of building footprints (15.27%) were found to be at high and very high risk, while 43.80% were at very low risk. Similarly, the cropland area affected by flooding was categorized into five risk classes, with 16.85% and 17.28% falling in the very high and high-risk categories, respectively.
This comprehensive understanding of flood hazards and associated risks can guide the development of effective mitigation strategies and management plans. By identifying the most susceptible areas, decision-makers and urban planners can prioritize targeted interventions, such as infrastructure upgrades, improved drainage systems, and enhanced emergency response procedures.
The synergistic integration of machine learning techniques and geospatial data analytics showcased in this study represents a significant advancement in flood hazard modelling. The generated flood maps will be invaluable for risk-informed planning and adaptation efforts in the Arambag region. This research contributes to the broader field of flood hazard mapping by demonstrating the power of data-driven approaches and the potential of cutting-edge technologies to enhance the accuracy and granularity of flood risk assessments.
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Tip: Regularly inspect and maintain flood barriers and drainage systems
