AI forecasting and risk models are being used to anticipate conflict, population change, crime, floods, refugee-service needs, severe weather and evacuation risks. Their outputs can help governments and aid organizations plan earlier—but the examples range from proof-of-concept research to operational services, and their reported scores measure different things.
Ten examples of AI forecasting and risk models
1. Conflict-risk forecasts in eastern Democratic Republic of Congo
The World Bank Group says a model forecasts changes in conflict levels in North Kivu, South Kivu and Ituri. The Bank reported up to 87% accuracy at a 150-day forecast horizon in 2026. It also identified social perceptions, economic pressures and conflict history as factors associated with forecast change.
As an Amazon Associate I earn from qualifying purchases.
The forecasts informed a risk and resilience assessment and a project that envisages financing triggers based on observed or forecast conflict levels. The Bank describes the models as proof-of-concept; their role in informing decisions does not by itself establish that a financing intervention caused better outcomes.
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
2. Population-change forecasts in the Ethiopia–Kenya–Somalia borderlands
A World Bank team used satellite imagery to identify built structures in 56 towns and cities as a proxy for population change. Its 2026 brief reports over 99.9% accuracy for identifying structures and up to 74% accuracy for forecasting population change as far as three months ahead. Those figures describe separate tasks: structure detection is not the same as forecasting population change.
#1 Best Overall
The findings informed the $330 million DRIVE project and the design of an Ethiopia displacement-risk model, according to the Bank. The reported figure for DRIVE is a project amount, not a model-performance measure.
3. Daily crime-change forecasts in an unnamed small-island state
Where official crime statistics were unavailable, a World Bank team used an agentic language model to produce crime data from online news reporting, then built a model to predict daily crime changes. The Bank reported 87.1% accuracy in 2026 and identified food prices and sentiment about women, youth, public services and governance among the factors associated with crime patterns.
The brief does not name the state or provide enough methodological detail to independently assess the score. Treat it as a reported result for this particular case, not a general estimate of how accurately AI can predict crime.
The Tool Desk
Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →4. Planning refugee services in Uganda
The World Bank says its Social Policy and Disaster Risk Finance team delivered a model to support a displacement-risk financing mechanism. The intended action chain is practical: a forecast of displacement risk can help the Government of Uganda plan to scale public-service capacity before refugees arrive.
Rank #2
The Bank’s brief does not quantify the model’s forecast performance or the effect of the financing mechanism. This is an example of a forecast supporting preparedness, not evidence of a measured reduction in service disruptions.
5. Flood preparedness in Adamawa State, Nigeria
Google reports that UN OCHA uses Google river-flood forecasts in an Anticipatory Action Programme. When forecast risk is high, the programme can prompt early measures such as preparing shelters. This is an operational decision workflow as described by the provider; the account does not supply an independent impact estimate.
6. Cash transfers before flooding in Kogi State, Nigeria
Google says GiveDirectly used its flood forecasts to send cash transfers before flooding. The reported purpose was to help families evacuate and buy protective equipment, including sandbags. This is a provider account of how a forecast informed action, not a quantified independent evaluation of the transfers’ effects.
Windows Errors? Fix Them Before They Spread
Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallCrashes, No Sound, or Screen Glitches?
Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minute7. Flood Hub’s public forecasts
Google says its Flood Hub forecast service covers areas at risk of significant flooding across more than 150 countries, with a potential reach of 2 billion people. These are Google-reported coverage figures, not a measure of forecast accuracy or proof that every person in those places receives an alert.
Google also describes a pilot with the World Meteorological Organization and national hydrology agencies in Czechia, Nigeria, Uruguay and Vietnam. The pilot is testing how local data affects forecasting in regional river basins, which highlights why global coverage and locally useful forecasts are not interchangeable.
8. NOAA’s operational global AI weather models
NOAA has launched AIGFS, AIGEFS and the hybrid ensemble HGEFS as operational global AI weather models. In a NOAA release published in 2025 and updated in 2026, the agency says 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 reports that HGEFS outperforms the AI-only and physics-only ensembles on most major verification metrics. The agency also notes limitations: AIGFS v1.0 has degraded tropical-cyclone intensity forecasts, and hurricane-intensity forecasts remain an improvement area for HGEFS. These are agency-reported results, not an independent validation across all forecast conditions.
9. Experimental hurricane guidance during Hurricane Melissa
The International Telecommunication Union (ITU) reports that the U.S. National Hurricane Center examined experimental machine-learning guidance alongside conventional dynamical models during the 2025 hurricane season. A Google experimental tropical-cyclone ensemble generated up to 50 track and intensity scenarios. ITU says a large portion of the ensemble’s 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. A scenario among ensemble members is not the same as a confirmed outcome or a single definitive prediction.
10. Tonga asset mapping and flood scenarios
The ITU describes an AI-assisted digital twin of Tongatapu that mapped buildings, mangroves and other assets, then supported realistic inundation scenarios for evacuation planning. The account gives no quantitative forecast-accuracy result, so its value here is as a planning tool rather than a scored forecasting system.
How to interpret the reported results
The figures above are not a shared leaderboard. Accuracy for detecting structures, accuracy for forecasting population change, computing resources, and the number of people or countries covered describe different properties. Even two accuracy figures may not be comparable if the target, time horizon, data, evaluation method or geography differs.
- Check what is being predicted. A forecast of daily changes, a 150-day conflict outlook and a flood-risk alert address different questions and support different decisions.
- Look for the action pathway. The practical value depends on whether an organization can act on a forecast—for example, by preparing shelters, arranging cash transfers, scaling services or planning evacuations.
- Distinguish maturity levels. A proof-of-concept, a pilot, experimental guidance and a routine operational service are not equivalent evidence of deployment or reliability.
- Separate a model’s output from its impact. A forecast may inform a decision, but demonstrating that the decision improved outcomes requires separate evidence.
Why data quality and oversight still matter
Forecasts are only as useful as the observations and systems behind them. The U.S. Government Accountability Office has identified sparse rural observations, trust and bias concerns, coordination challenges, and high development and operating costs as issues affecting AI use in weather forecasting. The ITU likewise recommends robust observation infrastructure, human oversight for life-safety decisions and clear accountability.
Best Value
These concerns matter especially when forecasts influence evacuation, aid or public-service decisions. Decision-makers need to know what a model predicts, how uncertain the result is, who is responsible for acting on it and what happens when local observations are incomplete or conditions change.
Other reported applications
The ITU also reports that an AI-generated population dataset at 100-metre resolution was used in Liberia’s Early Warning Connectivity Map to identify people exposed to floods who lacked mobile-alert coverage. In China, the MAZU system integrates satellite, radar and local models across risk assessment, monitoring, alert communication and preparedness guidance. These examples extend the use of forecasting and risk models into connectivity planning and multi-stage warning systems.
Separately, nPlan’s case-study index lists AI-led forecast and risk-management work associated with HS2/SCS JV, LNG Canada, the Transpennine Route Upgrade, a Suffolk hospital construction project, Network Rail and a government highways project. The listing establishes that the company names these cases; by itself, it does not establish independent outcomes or the deployment status of each project.
Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesQuick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
