AI can improve planning for climate-driven flood risks

Flood risk is escalating worldwide as climate change intensifies rainfall, accelerates snowmelt, and pushes rivers resulting from extreme weather events beyond the limits of traditional protection systems. Urban expansion into floodplains and the hardening of landscapes have further reduced the capacity of natural drainage, leaving many regions exposed to destructive, high-energy floodwaters. Against this backdrop, engineers and policymakers are increasingly questioning whether conventional single-structure defenses are sufficient for a future marked by uncertainty and extremes.

A new peer-reviewed study published in the journal AI offers a data-driven response, showing that artificial intelligence can play a decisive role in strengthening flood risk management when combined with hybrid defense systems that integrate gray infrastructure and nature-based solutions. The study, titled Ensemble Learning-Driven Flood Risk Management Using Hybrid Defense Systems, provides empirical evidence that explainable, ensemble-based AI models can accurately predict floodwater energy dissipation, a critical factor in limiting damage during flood events.

Why hybrid flood defenses are gaining urgency

The study is grounded in a growing consensus among hydrologists and urban planners that flood damage is driven not only by water depth but by the kinetic energy of moving water. High-energy flows erode embankments, undermine foundations, and intensify downstream destruction even when overtopping does not occur. Traditional defenses such as dikes and levees are often designed for specific flow conditions and can fail abruptly when those conditions are exceeded.

Hybrid flood defense systems aim to reduce this vulnerability by combining engineered structures with vegetated or roughened elements that slow water movement and dissipate energy. Dikes and moats provide containment, while vegetation increases surface roughness, reduces velocity, and absorbs part of the flood’s kinetic force. These systems are increasingly viewed as more adaptable and sustainable than purely concrete-based defenses.

However, designing effective hybrid systems presents a technical challenge. The interactions between flow velocity, roughness, vegetation density, and backwater effects are highly nonlinear. Physical experiments and numerical simulations can model these interactions, but they are costly, time-consuming, and difficult to scale across multiple design scenarios. This has limited their usefulness for rapid planning in a changing climate.

The authors position artificial intelligence as a solution to this bottleneck. By learning from experimental and observational data, AI models can capture complex relationships that elude simplified equations and deliver fast predictions that support infrastructure design and risk assessment.

How ensemble AI predicts floodwater energy dissipation

To test this approach, the study assembles a dataset of 136 observations drawn from published research and controlled laboratory experiments. The dataset captures a wide range of hydraulic and structural conditions relevant to hybrid defenses, including flow regime indicators, surface roughness, vegetation density, and relative backwater rise. The target variable is floodwater energy dissipation, a direct measure of how effectively a defense system reduces destructive force.

Several advanced AI models are evaluated, including Random Forest, Extreme Gradient Boosting optimized with Particle Swarm Optimization, Support Vector Regression with Particle Swarm Optimization, and Artificial Neural Networks optimized using Particle Swarm Optimization. These methods are selected for their ability to model nonlinear systems and interactions common in hydraulic processes.

The results show a clear performance hierarchy. Ensemble learning methods outperform single-model approaches, with the Random Forest model delivering the most consistent and accurate predictions across training, testing, and validation phases. It achieves high explanatory power and low prediction error, indicating strong generalization rather than overfitting.

Crucially, the study does not stop at prediction accuracy. It incorporates explainable artificial intelligence techniques to identify which physical parameters drive model outputs. This step is essential for real-world adoption, where engineers and regulators must understand the basis of AI-assisted decisions.

The explainability analysis identifies surface roughness as the most influential factor in floodwater energy dissipation. Vegetation density also plays a significant role, confirming the effectiveness of nature-based components within hybrid defenses. High flow intensity, by contrast, reduces overall system efficiency, highlighting that even optimized defenses have limits under extreme conditions.

By translating AI outputs into interpretable physical insights, the study bridges the gap between data science and engineering judgment. Designers can use these findings to prioritize interventions that deliver the greatest protective benefit and to recognize where additional reinforcement may be required.

Implications for climate-resilient infrastructure planning

The findings carry important implications for flood risk management as climate uncertainty grows. One of the key advantages of the AI framework is speed. Once trained, the models can evaluate thousands of design scenarios in minutes, compared with weeks or months for physical experiments or detailed simulations. This enables rapid sensitivity analysis, optimization, and stress testing under different climate projections.

The research also strengthens the case for integrating nature-based solutions into flood defense strategies. By quantifying the role of roughness and vegetation in energy dissipation, it provides empirical backing for policies that combine ecological restoration with engineered protection. Such approaches align flood management with sustainability goals, biodiversity preservation, and long-term cost efficiency.

From a governance perspective, the use of explainable AI is particularly significant. Infrastructure decisions involve public safety and substantial investment, and opaque black-box models can undermine trust. By clearly identifying the drivers of model predictions, the framework supports transparency, accountability, and evidence-based decision-making.

The authors emphasize that the methodology is scalable. While the study focuses on dikes, moats, and vegetated systems, the same approach can be extended to urban green infrastructure, floodplains, and coastal defenses. As more data become available, the models can be retrained to reflect local conditions and evolving climate risks.

The study also acknowledges limitations, including the size of the dataset and reliance on controlled experimental inputs. However, these constraints are presented transparently, and the authors argue that they do not diminish the core contribution. Instead, the research establishes a foundation for future work that integrates larger datasets and field observations.