Data-driven flood forecasting surges ahead, but uneven progress puts millions at risk

Data-driven technologies are emerging as powerful tools to predict and mitigate disasters such as floods. However, a new study reveals that while flood forecasting models have evolved significantly in recent years, major gaps in technology adoption, geographical coverage, and predictive capabilities remain.

The research, "Evolution of Data-Driven Flood Forecasting: Trends, Technologies, and Gaps—A Systematic Mapping Study" and published in Water, systematically reviews 363 studies published between 2019 and 2024 to provide an in-depth analysis of how machine learning and artificial intelligence (AI) are shaping flood forecasting while highlighting the challenges that must be addressed to build globally resilient systems.

How are data-driven models tansforming flood forecasting?

The study finds that machine learning (ML) and deep learning (DL) techniques have become central to modern flood forecasting. Traditional hydrological models are increasingly being replaced or enhanced by data-driven approaches capable of learning complex patterns from vast datasets. Among these, Long Short-Term Memory (LSTM) networks dominate, praised for their ability to handle temporal sequences and improve prediction accuracy.

In addition to LSTM models, hybrid and ensemble techniques are gaining momentum. These models combine multiple algorithms to leverage their strengths, resulting in improved performance and reliability. Their adoption has grown significantly over the review period, reflecting a shift toward more sophisticated forecasting strategies.

Despite these advancements, the study notes that most models remain heavily dependent on meteorological and hydrological data, primarily rainfall and water level measurements. While these inputs are critical, the authors argue that incorporating additional variables such as land use patterns, soil moisture, and remote sensing data could further enhance forecasting accuracy.

Where are the gaps in current flood prediction research?

Over half of the studies analyzed were conducted in East and Southeast Asia, with China alone contributing 36% of the total. While this concentration reflects the high flood risk in these regions, it also exposes a critical lack of research in other vulnerable areas, including Africa, South America, and the Middle East. These regions face growing flood threats but remain underrepresented in scientific studies, limiting the development of tailored forecasting solutions.

The study also identifies several methodological gaps. Graph-based modeling, which could capture the spatial relationships between interconnected river systems, remains largely unexplored. Similarly, transfer learning, a technique that enables models trained in data-rich environments to be applied in data-scarce regions, is underutilized despite its potential to bridge the gap between well-studied and neglected areas.

Uncertainty quantification is another area in need of greater attention. Most current models provide deterministic forecasts, offering single-value predictions without accounting for the range of possible outcomes. Without probabilistic approaches, decision-makers may struggle to fully assess flood risks and prepare accordingly. The authors emphasize that advancing uncertainty analysis is crucial for improving the reliability and usability of forecasting systems.

What needs to be done to improve global flood preparedness?

The study’s findings suggest that while data-driven technologies hold enormous promise, significant work remains to be done to ensure they deliver on their potential. The authors outline several key recommendations to guide future research and development.

First, expanding research beyond Asia is essential. Flood-prone regions in Africa, South America, and the Middle East need more targeted studies, data collection efforts, and model development to build localized forecasting systems. Without this expansion, global disparities in flood preparedness will persist.

Second, the study calls for greater integration of advanced AI architectures, such as Graph Neural Networks (GNNs) and transformer models, which can handle complex spatial and temporal data more effectively than existing algorithms. These technologies could revolutionize how floods are predicted, particularly in areas where data is fragmented or incomplete.

Third, the authors advocate for a stronger focus on probabilistic forecasting. By moving beyond single-value predictions, researchers can provide decision-makers with a range of possible scenarios, enabling better risk management and response planning. Coupling probabilistic forecasts with real-time data streams would further enhance the timeliness and accuracy of warnings.

Hybrid modeling approaches that combine physical hydrological models with AI-driven components offer a path toward more robust systems. Such models can leverage both the interpretability of physical models and the predictive power of data-driven techniques, resulting in forecasts that are not only accurate but also scientifically grounded.

To build truly resilient flood forecasting systems, researchers must expand their focus to underrepresented regions, embrace cutting-edge AI techniques, and prioritize models that account for uncertainty. Policymakers and disaster management agencies should also invest in data collection, infrastructure, and cross-border collaborations to ensure these technologies are implemented where they are needed most.