The core insight of the study is deceptively simple. Tides are not random. They are governed by the gravitational interplay between the Earth, the Moon and the Sun, producing regular cycles of rising and falling water that can be calculated years in advance. In many coastal locations, the tide determines whether a given storm surge is sufficient to push water levels above established flood thresholds. When a moderate surge coincides with a high tide, flooding follows; when the same surge arrives at low tide, water levels may remain safely below the threshold. As Haigh explains, even storm-driven flooding often inherits the timing of the tides, because the tide acts as the gatekeeper that decides whether the surge crosses the critical line.

What makes the new work distinctive is its focus on the hour of the day rather than the season or the year. Previous research on flooding has concentrated heavily on seasonal patterns, examining, for example, why river floods cluster in certain months or why coastal floods are more frequent during particular times of the year. Haigh notes that surprisingly little attention has been paid to what hour of the day flooding actually occurs. Part of the reason, he argues, lies in how flooding is measured. Flood impacts are commonly summarized as the number of flood days per year, and once an event is compressed into a single day, the information about whether the flooding happened at 3 a.m., 8 a.m. or 3 p.m. simply disappears. By returning to the underlying records and preserving the temporal detail, the research team was able to recover patterns that conventional flood statistics had erased.

The regional results are striking in their specificity. In Boston, floods occur predominantly around noon and midnight, mirroring the timing of that harbor’s high tides. In Southern California, flooding is more common in the morning, again tracking the local tidal cycle. In Florida, the picture is more varied, because tidal behavior differs substantially from one stretch of coastline to another. Along the Atlantic Coast and down toward the Florida Keys, coastal flooding is moderately predictable in its timing, while along the Gulf Coast, where tidal ranges are smaller and more complex, flooding is less predictable. These differences matter, because they show that the daily fingerprint of flooding is not a universal pattern but a local one, shaped by the specific tidal regime of each coastline.

The study also establishes that the phenomenon is not confined to places with semidiurnal tides, the familiar pattern of two high tides and two low tides each day that characterizes much of the U.S. East Coast and the U.K. The researchers found that predictable daily timing also emerges in locations with diurnal tidal systems, which experience only one high tide and one low tide per day. The reason lies in the way different tidal signals interact. Tides are generated by multiple astronomical constituents, each with its own period, and their superposition produces the observed water-level cycle. Whether a coastline experiences two highs a day or one, the resulting pattern remains regular enough that flood events, which require water levels to cross a threshold, cluster around the recurring peaks of that cycle.

The technical implication is that flood risk is not evenly distributed across the hours of a day, even when the weather is. A storm system that lingers offshore for twelve hours does not pose a uniform threat throughout its passage. Its most dangerous moments are those that coincide with high tide, and in many locations those moments can be identified in advance with the same precision used to publish tide tables. This reframing has consequences for how flood forecasting is constructed. Traditional forecasts emphasize the magnitude of expected water levels, but the timing of when those levels are most likely to be exceeded is equally important for operational decisions, from closing roads to repositioning emergency assets.

Haigh argues that this matters because timing influences impacts. The same flood can have very different consequences depending on whether it occurs during the morning commute, during business hours or in the middle of the night. A flood that peaks at rush hour can strand commuters, snarl traffic and endanger drivers who attempt to cross inundated roadways. A flood of identical height at 3 a.m. may cause comparable physical damage but far fewer casualties and disruptions, simply because fewer people are exposed. Understanding these temporal patterns, the study suggests, could improve flood forecasting, emergency planning, infrastructure operations and long-term coastal adaptation. Utilities, hospitals, schools and transit agencies could all incorporate the known tidal windows into their contingency planning, aligning preparedness measures with the hours when flooding is statistically most likely.

The research arrives at a moment when the baseline for coastal flooding is shifting. As sea levels continue to rise, coastal flooding is increasingly occurring without storms to spur it on, a phenomenon often described as nuisance or sunny-day flooding, in which exceptionally high tides alone are enough to inundate low-lying streets. Haigh points out that higher mean sea levels mean it takes only a relatively small weather contribution, or sometimes simply a very high astronomical tide, to cross a flood threshold. In such conditions, the timing of the tides becomes even more decisive, and understanding exactly when those critical tidal windows occur could become an increasingly important component of coastal flood forecasting and preparedness.

The practical payoff of the work could be substantial. Haigh says the findings could make existing flood forecasts more predictable, providing more warning before disasters occur. That added lead time can help residents, businesses and emergency managers anticipate disruption and plan accordingly, whether that means scheduling deliveries around high-tide windows, warning drivers before morning inundations or pre-positioning pumps and barriers. Because the tidal component of flood timing is deterministic, it can be computed far in advance, offering a layer of foresight that weather-driven forecasting alone cannot match. The uncertainty in a flood forecast then narrows to the meteorological contribution, while the tidal contribution is already known.

The study, titled Coastal flooding at predictable hours and published in Nature Communications on 28 July 2026, represents a collaboration among international researchers and reflects Haigh’s long career in sea level and coastal impacts. A professor who joined the University of Central Florida in 2025 as director of the Center for Integrated Coastal Research, Haigh previously held the chair in sea level and coastal impacts at the University of Southampton and directed the Center for Doctoral Training for Resilient Flood Futures. His team’s work suggests a quiet but consequential shift in coastal science: the hazard that communities have treated as random now reveals a schedule, and learning to read that schedule may prove one of the most practical tools available as rising seas make the ocean’s high-water moments an ever more frequent visitor to the world’s coastlines.

Subject of Research: Predictable daily timing of coastal flooding driven by tidal cycles

Article Title: Study shows coastal flooding occurs at predictable times

Article References: Study shows coastal flooding occurs at predictable times. (n.d.). Original publication

Image Credits: AI Generated

DOI: Not provided

Keywords: coastal flooding, tides, sea level rise, flood forecasting, storm surge, tidal cycles, Nature Communications, University of Central Florida, emergency planning, nuisance flooding, tide gauges, coastal adaptation

The core idea borrows directly from nature. Real ants deposit pheromones along the trails they walk, and subsequent ants prefer paths carrying stronger chemical signals, a positive feedback loop that gradually steers the colony toward the shortest route to food. The ant colony optimization algorithm encodes this behavior in software: a population of virtual ants moves across a grid map, probabilistically choosing the next node based on pheromone concentration and a distance-based heuristic, then reinforcing the routes it traverses in proportion to their length. Over many iterations, the swarm converges on short paths. The method is prized for its robustness, distributed computing character, and adaptability, and it has already found use in robot navigation, transportation networks, and urban fire rescue.

But the standard algorithm has a blind spot when applied to burning villages. Its original heuristic measures nothing but geometric distance, which means it can happily route evacuees through an alley filled with lethal smoke simply because that alley happens to be short. Qiu and colleagues redesigned the heuristic to weigh three additional factors alongside raw distance: a human activity index that combines air temperature, toxic gas concentration, and crowd density through piecewise penalty functions; a passage difficulty coefficient that penalizes alleys narrower than three meters and effectively blocks those narrower than 1.5 meters with a penalty factor of 1000; and the remaining distance to the destination. The resulting equivalent evacuation length converts each grid cell into a risk-adjusted cost, so virtual ants automatically detour around high-temperature zones, CO-heavy corridors, and congested passages without any manual route instruction.

The fire physics feeding this risk map came from Pyrosim, a computational fluid dynamics tool. The team first built a three-dimensional model of Ma’an Village in Revit, capturing more than 30 ancient buildings, the road network, vegetation, and firefighting facilities in detail. They then imported that model into Pyrosim and simulated a five-megawatt fire ignited outside the village’s central drum tower over a 240 by 200 meter domain at half-meter resolution, some 6.1 million grid cells in total. Sensors placed at two meters in height at each exit recorded temperature, visibility, and smoke concentration. The simulation showed the fire source reaching 130 degrees Celsius within 50 seconds; by 500 seconds, the blaze had spread to nearby buildings, visibility had dropped below 13 meters across a hazardous core near the fire, and smoke concentration had climbed to 4.5 times 10 to the negative fifth mol per mol, well beyond the safety threshold.

The researchers extracted temperature, gas concentration, and crowd density data at two static snapshots, 200 and 500 seconds, and mapped them onto a 30 by 30 evacuation grid of 1.5 by 1.5 meter cells. Cell size matters here: the authors emphasize that grid cells represent the longitudinal step of a passage, not its transverse width, so genuinely impassable alleys are excluded by the passage difficulty coefficient rather than by grid resolution. This BIM-to-Pyrosim-to-MATLAB pipeline creates a closed data loop in which architectural geometry, fire field data, and route optimization flow automatically from one platform to the next, without repeated manual conversion, and the framework is explicitly designed as a generalized method for any historic dense settlement with similar characteristics, not just one village.

Three further upgrades sharpened the algorithm itself. An adaptive pheromone concentration steps down through the run, from 100 early on to 75 and then 50, boosting exploration at the start and refining convergence later. An adaptive evaporation coefficient gradually declines until it hits a floor tied to the fire’s impact level, with higher severity driving higher minimum evaporation to keep the swarm from fixating on stale routes. And a backtracking strategy rescues any ant that wanders into a dead end: the blocked node joins a taboo list and the ant retreats until it finds a passable cell, preventing infinite loops in the maze-like village geometry. After systematic single-factor tuning of the pheromone and heuristic weights, the optimal configuration settled at alpha equal to 2, beta equal to 9, evaporation factor 0.4, 70 ants, and 100 maximum iterations.

The performance gains were substantial and statistically solid. Running each parameter combination across 25 independent random seeds to eliminate stochastic noise, the team found that the improved algorithm shortened the shortest physical escape path from S1 to exit E4 from 17.75 meters under the standard algorithm to 15.11 meters under the improved version, a 14.9 percent reduction, the largest decrease among all seven tested exit paths. Every planned route came out shorter with the improved method, and paired-sample t-tests confirmed the differences were significant at p less than 0.05. Convergence improved even more dramatically: the upgraded algorithm locked onto optimal routes within an average of 3 to 10 iterations, while the standard version needed about 18, a gap significant at p less than 0.01, and its late-stage curve fluctuations indicated a stronger ability to avoid premature convergence on local optima.

When the simulated fire reached its spread phase, with 19 fire grids covering 42.75 square meters and four of seven exits blocked, the framework adapted cleanly, rerouting evacuees to the three remaining passable exits and still identifying exit E4 as the shortest at 15.11 meters. Computationally, the method is light: a single route calculation for the entire village takes roughly 28 seconds on an ordinary 16-gigabyte desktop with about 4.2 gigabytes of memory, and enlarged grids slow convergence slightly without stalling, suggesting the approach scales to larger heritage sites. The authors stress that all geometric parameters came from field measurements of the real village, grounding the virtual experiment in authentic conditions.

The team is candid about the work’s limits. The simulation covers only carbon monoxide as a toxic gas, excludes CO2, hypoxia, hydrogen cyanide, structural collapse, and wind effects, and relies on two static fire snapshots rather than continuous dynamic re-planning as occupants move. Validation so far rests on a single village and BIM-based co-simulation, with no field evacuation drills or historical fire records to calibrate the model, and the full route optimization currently targets physical distance alone without quantifying route safety. The authors also note that a complete, standardized fire risk ontology remains future work. Still, the framework offers heritage managers and emergency planners an immediate practical tool: an offline way to draft evacuation plans for the world’s many vulnerable historic settlements, with real-time sensor integration and dynamic occupant modeling as the clear next step toward protecting both lives and irreplaceable cultural heritage.

Subject of Research: Optimization of fire evacuation routes in historic dense settlements using BIM and an improved ant colony algorithm

Article Title: Generalized optimization method for fire evacuation routes in historic dense settlements based on improved ant colony algorithm and BIM

Article References: Qiu, J., Qin, J., Hu, Y., Liu, Y., & Qin, J. (2026). Generalized optimization method for fire evacuation routes in historic dense settlements based on improved ant colony algorithm and BIM. Discover Artificial Intelligence, 6(1), Article 1214. https://doi.org/10.1007/s44163-026-02256-2

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02256-2

Keywords: fire evacuation, historic dense settlements, ant colony optimization, BIM, path planning, Pyrosim, Revit, Ma'an Village, heuristic function, adaptive pheromone, emergency planning, cultural heritage