Cloud AI Framework Maps Flood Danger Where Gauges and Models Are Missing

Floods are among the most widespread and costly natural hazards on Earth, and the losses keep climbing as people and infrastructure crowd into flood-prone areas. Between 1985 and 2015, global settlement in the highest-hazard flood zones expanded by roughly 122 percent, far outpacing the 85 percent growth of total settlement extent. The 2019 floods in Iran alone affected approximately ten million people and caused an estimated 4.27 billion US dollars in damage. Yet many of the regions hit hardest lack the gauge records, surveyed terrain data and calibration observations that conventional flood modelling demands. A new open-access study in Results in Engineering presents SIAVASH, a solver-free, cloud-native framework that combines Earth observation and machine learning to produce design-event flood-hazard maps and screen infrastructure weak lines in exactly these data-scarce settings.

Developed by Afshin Fouladi Semnan and Mohammad Javad Ostad Mirza Tehrani, SIAVASH, short for Smart Integrated Assessment of Vulnerability for Advanced Storm-Induced Flood Hazards, deliberately avoids running a site-specific two-dimensional hydrodynamic model. Instead, it harmonises open geospatial data on a 30-metre analysis grid and trains machine-learning models to emulate the spatial patterns of flood-depth products from the Copernicus GloFAS global flood-hazard maps. The authors stress that these targets are model-derived reference outputs, not observations, and that the framework is intended for comparative engineering screening and prioritisation rather than as a replacement for locally calibrated hydraulic modelling where feasible.

The framework was tested in two Iranian urban domains that experienced severe flooding in March and April 2019 but represent starkly contrasting terrain. Aqqala, in Golestan Province, sits on a low-relief Caspian floodplain where slow flood spreading and prolonged ponding dominate; a three-day rainfall total of 338 millimetres during the 2019 event sequence was equivalent to roughly half the local mean annual rainfall. Poldokhtar, in Lorestan Province, occupies a confined Zagros valley where 142 millimetres fell in 24 hours, an event with an estimated return period of about 56 years, driving rapid river-level rise and concentrated overbank flooding along a corridor packed with roads and rail infrastructure.

Eighteen predictor bands were assembled from open sources, spanning terrain morphometry derived from SRTM elevation data, hydrographic and infrastructure proximity from OpenStreetMap, land cover and soil indicators from ESA WorldCover and OpenLandMap, and hydroclimatic conditions from CHIRPS rainfall records, including a three-month Standardised Precipitation Index. Six regression algorithms competed under a common nested cross-validation scheme: Random Forest, XGBoost, LightGBM, support vector regression, k-nearest neighbours and a multilayer perceptron neural network. XGBoost won in both basins. In Aqqala its out-of-fold RMSE was a remarkably low 0.0526 metres with an R-squared of 0.9878, while in Poldokhtar it achieved a mean RMSE of 0.6137 metres and an R-squared of 0.9600 across the 30-, 100-, 300- and 1000-year design events.

The team then did something unusually honest for a machine-learning flood study: they stress-tested those impressive numbers. When cross-validation was repeated with 1.2 by 1.2 kilometre spatial blocks instead of shuffled pixels, RMSE rose by 372 to 458 percent in Aqqala and by 109 to 124 percent in Poldokhtar, confirming that neighbouring raster cells share terrain and target characteristics and inflate ordinary validation scores. More decisively, reciprocal leave-one-basin-out testing, in which a model trained in one basin was applied directly to the other, failed in both directions, producing negative R-squared values and direction-dependent biases of up to 9.5 metres. The message is clear: strong within-basin performance does not transfer to a basin with contrasting terrain, and new applications require local training data or explicit domain adaptation.

Interpretability came through SHAP and pairwise TreeSHAP analysis, which revealed distinct attribution structures in each basin. In Aqqala, the three-month precipitation index was the dominant predictor, followed by the Height Above Nearest Drainage index and river-network density, and the strongest interactions linked rainfall with drainage proximity. In Poldokhtar, filled-DEM elevation dominated at every return period, followed by distance to river and HAND, with elevation-rainfall interactions growing sharply toward the 1000-year event. The authors caution that these attributions describe the fitted models, not causal hydrology, but they pinpoint exactly where better data would most improve predictions.

SIAVASH also connects rapid depth mapping to infrastructure-sensitive screening through an automated weak-line analysis. Candidate weak lines, geometrically plausible reaches where an assumed complete blockage could alter water-surface connectivity, were extracted by intersecting a 25-year detection footprint with design-event footprints, retaining only segments longer than 500 metres. Nineteen segments were retained in each basin, and exhaustive enumeration of all closure subsets produced 2116 accepted scenarios in Aqqala and 308 in Poldokhtar. A deterministic Water-Surface-Elevation Priority-Fill routine then simulated how assumed closures redistribute water, yielding maximum local depth increases of up to 1.7 metres in Aqqala and 6.2 metres in Poldokhtar. These are connectivity stress tests, not predictions of embankment failure, and the resulting envelopes do not represent any single physically co-occurring closure state.

The depth and velocity-proxy fields were converted into hazard classes using two established conventions: the Swiss FOEN danger matrix, which combines intensity with event probability, and the Australian Disaster Resilience Handbook 7 framework, which jointly evaluates depth, velocity and the depth-velocity product. Under the predefined reporting rule, the depth-velocity basis produced the larger severe-hazard footprint in both basins, and it mattered dramatically in Aqqala, where depth alone yielded no Significant cells at all while the combined metric identified roughly 14 percent of the classified footprint. Poldokhtar was already dominated by severe hazard, with FOEN Significant classes covering about 65 percent and ADR7 very-high-to-extreme classes covering 93.5 percent of its classified area, leaving little room for closure-related escalation. Aqqala showed the clearer closure response, with its ADR7 H5-H6 footprint rising from 37.54 to 39.33 percent.

An independent benchmark against Sentinel-1 radar imagery of the 2019 floods added further nuance. The modelled footprints captured most satellite-detected wet cells, with recall between 0.84 and 0.96, but precision was low, around 0.24 to 0.27, meaning the modelled wet area was three to nearly four times larger than the satellite-derived extent. Raising the wet-depth threshold from 0.05 to 0.20 metres barely improved agreement, indicating that the excessive extent reflects deeper issues such as design-event versus historical-event mismatch, digital elevation model artefacts and the limited representation of channels and levees in global datasets, rather than a simple threshold problem. The authors interpret the satellite comparison as a cautious plausibility check, not a validation of predicted depth.

The study’s lasting contribution may be its template for scientific candour. By reporting shuffled-pixel, spatial-block and cross-basin results side by side, SIAVASH shows exactly where a solver-free machine-learning approach can be trusted, where it degrades and where it fails outright, while fold-ensemble diagnostics, covariate-range exceedance maps and weak-line-sensitive locations together tell practitioners where field surveys and better geodata would pay off most. For the many flood-exposed regions of the world that lack gauge networks and calibrated models, that transparency, combined with a fully cloud-native workflow built on open data, offers a practical starting point for land-use screening, emergency planning and prioritising the detailed hydraulic investigations that remain the gold standard.

Subject of Research: A solver-free, cloud-native Earth observation and machine-learning framework for flood-hazard mapping and weak-line connectivity screening in data-scarce regions

Article Title: SIAVASH: A solver-free, cloud-native EO–ML framework for weak-line screening and flood hazard mapping in data-scarce regions

Article References: Fouladi Semnan, A., & Ostad Mirza Tehrani, M. J. (2026). SIAVASH: A solver-free, cloud-native EO–ML framework for weak-line screening and flood hazard mapping in data-scarce regions. Results in Engineering, 32, Article 113357. https://doi.org/10.1016/j.rineng.2026.113357

Image Credits: AI Generated

DOI: 10.1016/j.rineng.2026.113357

Keywords: flood hazard mapping, machine learning, XGBoost, Earth observation, GloFAS, Sentinel-1, SHAP interpretability, cloud computing, data-scarce regions, Iran floods, weak-line screening, hazard classification