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Good practice Imported

NIHSA Annual Flood Outlook — Nigeria's AI-Assisted Flood Risk Forecasting Across 250+ Monitoring Stations

Nigeria · Abuja · See the Nigeria profile · See the Abuja profile

Evidence: Descriptive / self-reported Top 83% 40/100 · Ask Evidence Copilot about this practice

Nigeria's Hydrological Services Agency says it deployed deep-learning models trained on 200+ years of flood data for its 2026 Annual Flood Outlook, citing ~88% precision and flagging 14,118 high-risk communities — widely reported but not independently verified.

88 %
Forecast precision claimed for AI/deep-learning flood models (2026)
14118 communities (266 LGAs, 33 states + FCT)
Communities classified as high flood risk in the 2026 Annual Flood Outlook (2026)
15597 communities (405 LGAs)
Communities classified as moderate flood risk in the 2026 Annual Flood Outlook (2026)
NIHSA Annual Flood Outlook — Nigeria's AI-Assisted Flood Risk Forecasting Across 250+ Monitoring Stations

Details

Promoter
Nigeria Hydrological Services Agency (NIHSA)
Period
2026 (reported); ongoing Annual Flood Outlook programme
Keywords
flood forecasting, disaster risk management, hydrology, deep learning, early warning

Context

NIHSA, Nigeria's federal hydrological forecasting agency, produces an Annual Flood Outlook (AFO) that classifies local government areas (LGAs) into high, moderate and low flood-risk categories each year. In May 2026, NIHSA's Director-General reported that the agency had deployed artificial intelligence and deep-learning models trained on more than 200 years of historical flood data, with forecast precision reportedly rising to about 88%. The 2026 Outlook also introduced a new community-based flood reporting system and a flood micro-insurance scheme.

Results

The 2026 Outlook identified 14,118 communities across 266 LGAs in 33 states and the Federal Capital Territory as high flood risk, and a further 15,597 communities across 405 LGAs as moderate risk. NIHSA's Director-General stated that forecast precision has risen to about 88%, which he said surpasses World Meteorological Organization standards.

Implementation
Indicative cost
Medium (€50k–€500k)
Time to results
Long (> 3 years)

Commonly funded by

National / regional programmes

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