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AI forecasting and risk models are being used to anticipate conflict, population shifts, crime changes, floods, severe weather and disaster impacts. Their outputs can help governments and aid organizations prepare services, move cash or plan evacuations—but the evidence varies: some examples are operational services, while others are pilots, experiments or proof-of-concept models.
Where are AI forecasting and risk models being used?
The examples below show what each model predicts, how its output is used and what evidence its source reports. Reported accuracy figures describe different tasks, so they are not a shared leaderboard: identifying buildings is not the same as forecasting population change, and neither is comparable to measuring computing use or geographic coverage.
| Deployment and status | What it predicts or maps | How the output is used | Evidence and qualification |
|---|---|---|---|
| 1. Conflict risk in eastern Democratic Republic of Congo World Bank proof-of-concept |
Changes in conflict levels in North Kivu, South Kivu and Ituri. | Informs a risk and resilience assessment and a project that envisages financing triggers based on observed or forecast conflict levels. | The World Bank Group reports up to 87% accuracy at a 150-day horizon. Social perceptions, economic pressures and conflict history were associated with forecast change. This supports decision-making; it does not establish that the financing approach itself reduces conflict. |
| 2. Population change in the Ethiopia–Kenya–Somalia borderlands World Bank model |
Satellite imagery identifies built structures in 56 towns and cities as a proxy for population change, then forecasts population change. | Findings inform the $330 million DRIVE project and the design of an Ethiopia displacement-risk model. | The World Bank Group reports over 99.9% accuracy for identifying structures and up to 74% accuracy for forecasting population change as far as three months ahead. These are separate measures of separate tasks. |
| 3. Daily crime changes in an unnamed small-island developing state World Bank model |
An agentic language model uses online news reporting to produce crime data where official crime statistics were unavailable; a second model predicts daily changes. | Provides a way to analyze crime trends where official statistics are missing. | The World Bank Group reports 87.1% accuracy for predicting daily crime changes and identifies food prices and sentiment about women, youth, public services and governance among possible drivers. The brief does not name the state or provide enough methodological detail to independently assess the accuracy figure. |
| 4. Refugee-service capacity in Uganda World Bank-supported mechanism |
Displacement risk ahead of refugee arrivals. | The Government of Uganda can use the financing mechanism to plan and scale public-service capacity before refugees arrive. | The World Bank says its Social Policy and Disaster Risk Finance team delivered the model. The cited brief gives no forecast-performance metric or measured effect of the financing mechanism. |
| 5. Flood anticipatory action in Adamawa State, Nigeria Operational workflow described by Google |
River-flood risk. | Google reports that UN OCHA uses forecasts in an Anticipatory Action Programme, where high forecast risk triggers early steps such as preparing shelters. | This describes an operational decision workflow as reported by Google; the cited account does not provide an independent impact estimate. |
| 6. Pre-flood cash transfers in Kogi State, Nigeria Use reported by Google |
Flood risk before flooding. | Google reports that GiveDirectly used forecasts to send cash transfers before flooding, helping families evacuate and buy protective equipment such as sandbags. | The account is from Google and does not provide an independently measured impact estimate. |
| 7. Flood Hub public forecast service Public service and regional pilot |
Areas at risk of significant flooding. | Google says Flood Hub provides forecasts across more than 150 countries, covering 2 billion people. A separate pilot with the WMO and national hydrology agencies in Czechia, Nigeria, Uruguay and Vietnam tests how local data affects forecasts in regional river basins. | The country and population figures are Google-reported coverage claims, not measures of forecast accuracy. The pilot is testing the role of local data. |
| 8. NOAA global AI weather models Operational model suite |
Global weather forecasts using AIGFS, AIGEFS and the hybrid ensemble HGEFS. | Provides weather guidance; the hybrid ensemble combines AI and physics-based approaches. | NOAA reports that one 16-day AIGFS forecast uses 0.3% of the computing resources used by operational GFS and takes about 40 minutes; AIGEFS uses 9% of operational GEFS resources. NOAA says HGEFS outperforms the AI-only and physics-only ensembles on most major verification metrics. NOAA also notes degraded tropical-cyclone intensity forecasts in AIGFS v1.0 and says hurricane-intensity forecasting remains an improvement area for HGEFS. These are agency-reported results, not independent validation. |
| 9. Hurricane Melissa guidance Experimental guidance examined by forecasters |
A Google experimental tropical-cyclone ensemble generated up to 50 track and intensity scenarios during Hurricane Melissa. | The U.S. National Hurricane Center examined the experimental machine-learning guidance alongside conventional dynamical models. | ITU reports that a large portion of the ensemble members projected that Melissa could intensify to Category 5 in October 2025. This was probabilistic decision support considered by forecasters, not a standalone operational forecast. |
| 10. Tongatapu asset mapping and flood scenarios AI-assisted digital twin |
Buildings, mangroves and other assets, alongside realistic inundation scenarios. | The mapped assets and scenarios support evacuation planning. | ITU describes the planning use but provides no quantitative forecast-accuracy result. |
How should you interpret the reported results?
Start with the target and the decision it can support. A forecast of daily crime changes addresses a different planning problem from a long-range conflict forecast or a weather model generating multiple possible storm tracks. Likewise, a model can be useful because it helps trigger an action before a hazard occurs, even when its source has not published a comparable accuracy figure.
Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute- Accuracy is task-specific. A structure-detection score says how well imagery identifies buildings; it does not establish how accurately a model forecasts population change. Computing efficiency and geographic coverage describe still different things.
- Forecasts matter through an action pathway. Examples include preparing shelters, sending cash before flooding, scaling services ahead of refugee arrivals and planning evacuations. A reported use does not, by itself, prove that the intervention caused better outcomes.
- Deployment status changes what a result means. A proof-of-concept, a pilot, experimental guidance and an operational service are not interchangeable. Experimental scenarios can inform a forecaster without replacing official guidance.
- Source and evaluation matter. Most outcome and performance claims in these examples come from the organizations that developed or operate the systems. The available accounts do not provide consistent independent evaluations across deployments.
What can make these systems unreliable or difficult to govern?
Forecast quality depends on the observations and reporting available to the model, as well as on how the output is used. GAO has noted sparse rural observations, trust and bias concerns, coordination challenges, and high development and operating costs in AI forecasting. These issues can limit performance or make it harder to determine who is accountable when forecasts inform consequential decisions.
#1 Best Overall
For decisions involving safety or access to essential services, a forecast should support—not obscure—human responsibility. ITU recommends robust observation infrastructure, human oversight for life-safety decisions and clear accountability. Decision-makers also need to understand what a model predicts, its limits and what action a forecast is authorized to trigger.
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