AI Image-Generation Techniques Power a New Hourly Flood Forecasting Model

Flash floods are among the most violent and least predictable natural disasters on the planet. They account for roughly 85 percent of all flooding events and are responsible for thousands of deaths worldwide every year, according to figures cited by the World Meteorological Organization. In the United States, the National Weather Service notes that water levels in a flash flood can surge by as much as 30 feet and then recede back to normal within a single, chaotic day. The speed of these events is precisely what makes them so lethal: a community can be transformed in minutes, and the devastation can linger for years. Now, a team of scientists led by researchers at Penn State has borrowed a surprising set of tools from an unlikely corner of artificial intelligence — the very techniques that power image generators — to build a forecasting model capable of predicting flood risk on an hourly scale, the timeframe where flash floods actually live and kill.

The research, published in the journal Water Resources Research, describes a diffusion-based probabilistic modeling framework for hourly streamflow prediction and data assimilation. The team trained their model on rainfall and streamflow data collected from 516 river basins across the continental United States between 1990 and 2003, and then tested it on a window of data spanning 2009 to 2014 — a period the model had never encountered during training. According to the researchers, the diffusion approach generally outperformed the best existing hourly deep-learning models, particularly in capturing how much water flows through rivers during high-flow periods, which are notoriously difficult to anticipate. The work was led by Chaopeng Shen, professor of civil and environmental engineering at Penn State and corresponding author on the paper, whose group has spent years applying machine learning to national-scale flood forecasting.

The connection between image generation and flood prediction may sound whimsical, but the underlying mathematics is deeply relevant to hydrology. Generative AI models must be primed with existing information called training data, and they make predictions based on patterns observed in previous data sets. Shen describes the process as giving a model an image or a sentence with a portion missing and asking it to predict the content in that unknown region. Before the age of AI, scientists had to manually assimilate data, relying on numerous assumptions and what Shen calls mathematical gymnastics to make predictions. The explosion of AI science over the last decade has fundamentally changed how hydrologists — the scientists who study how water moves through the environment — approach forecasting, allowing them to assimilate information far faster and more easily using generative models trained on existing data.

The new model differs in important ways from the large language models that power conversational chatbots. Instead of text, it incorporates diffusion, a training method most commonly used in image generation. In diffusion training, a model first receives a complete dataset or picture. Once that clear data has been processed, noise — randomized, unpredictable information — is deliberately introduced into the dataset. The AI model is then tasked with removing the excess noise and reproducing an interpretable dataset or photo. Through countless iterations of this corruption-and-reconstruction cycle, the model learns the statistical structure of the data so thoroughly that it can generate realistic new examples or fill in missing information. For hydrologists, that reconstruction skill translates directly into the ability to reason about messy, incomplete, and uncertain environmental measurements.

This training approach turns out to be particularly effective for flood prediction because weather data such as rainfall can be extraordinarily unpredictable on an hourly basis. The diffusion framework allows the model to quantify uncertainty in its predictions, and it reduces doubt by assimilating recent gauge observations into the dataset through a process known as inpainting. Inpainting, in the image-generation world, means filling in a missing or corrupted region of a picture; in the flood-forecasting world, it means injecting fresh, near-real-time measurements into the model without the need for retraining. That distinction matters enormously for operational forecasting, where retraining a model on new data is often too slow and too expensive to be practical during an unfolding emergency.

Shen explains the mechanics with a concrete example. The basic training inputs include hourly rainfall, projected streamflow — the rate at which water flows in a river — and all the data collected from previous patterns. But the model can also accept new, almost real-time information. Given the daily streamflow recorded yesterday and the hourly rainfall observed today, the model can be asked to predict what the hourly peak water level might be today. This ability to fold the latest gauge readings into an ongoing prediction, without retraining, gives forecasters a continuously updated picture of flood risk, exactly the kind of rapid situational awareness that flash flood response demands.

The motivation for moving from daily to hourly forecasting is rooted in hard experience. Shen’s team had previously delivered a daily flood forecasting model that made accurate predictions using historical rainfall and streamflow, but it struggled to capture the severity of rapid weather swings that produce flash floods. Many of the most devastating floods in recent memory, including the flooding that swept across Central Texas in the summer of 2025, swell and recede in a matter of hours. On a daily averaging scale, Shen notes, water levels might still seem elevated but not disaster-inducing, even as an extraordinary peak races through a river channel. By calibrating the model for an hourly scale, the team can more reliably capture those short-lived but catastrophic surges that wreak havoc on communities.

The versatility of the diffusion framework extends beyond forward prediction. Shen explains that the model can also run in reverse: given the streamflow recorded at a river gauge, it can estimate the hourly rainfall that most likely produced that flow. This inverse capability offers another layer of utility for hydrologists, potentially helping to reconstruct rainfall events in basins where precipitation measurements are sparse or unreliable, and to validate observations against what the rivers themselves recorded. To verify the accuracy of their predictive model across the country’s river basins, the team built a map visualizing each basin’s Nash-Sutcliffe Efficiency, a standard metric used to assess the performance of hydrological models, with darker dots indicating less accurate predictions and lighter dots indicating more accurate ones.

The research team includes, in addition to Shen, several Penn State contributors: Wencong Yang, a postdoctoral researcher; Yalan Song, assistant research professor of civil and environmental engineering; Kathryn Lawson, a research associate; and civil and environmental engineering doctoral candidates Haoyu Ji and Leo Lonzarich. Ming Pan of the Scripps Institution of Oceanography at the University of California, San Diego, also contributed to the work. The effort was supported by the Cooperative Institute for Research to Operations in Hydrology and the National Oceanic and Atmospheric Administration Cooperative Agreement, with additional funding from the California Department of Water Resources.

Looking ahead, the team plans to continue development, investigating the benefits of different hybrid approaches to training AI models and building models that incorporate more physical variables into their predictions. For Shen, however, the ultimate objective is not about crowning a single best model. He observes that no single approach stands out as superior — each model has its own aspect it handles most comfortably. Instead, he frames the mission in human terms: the goal is to deliver a forecasting system that is robust, reliable, and capable of helping save people’s lives, and he believes the field is making real progress in that direction. As generative AI techniques mature and merge with the physical sciences, the same machinery that conjures fantastical images on demand may increasingly stand guard over riverside communities, translating noisy streams of rainfall and gauge data into the precious minutes that separate warning from tragedy.

Subject of Research: Diffusion-based generative AI for hourly flash flood and streamflow prediction

Article Title: The techniques AI uses to create images can help predict floods, researchers say

Article References: The techniques AI uses to create images can help predict floods, researchers say. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: flood forecasting, generative AI, diffusion models, hydrology, streamflow prediction, flash floods, machine learning, data assimilation, inpainting, Penn State, Water Resources Research, river basins