evidoria

Browse practices

Topic: Early-warning systems clear · back to the knowledge map

This selection at a glance

12
matching practices
8%
strong evidence, of 12 assessed

Evidence mix

Quasi-experimental 1
Observational / pre–post 4
Descriptive / self-reported 7

Most practices in: Kenya (2) · Bangladesh (1) · Fiji (1)

Results

12 result(s) Download CSV Get alerts for this search Cards List
SelectTitleEvidenceStandingTypeLocationScale
AI-Based Dengue Early Warning System — Singapore NEA's Machine-Learning Outbreak Forecasting Evidence: Quasi-experimental Top 6% 87/100 Good practice Singapore, Singapore —
Google Flood Hub — Bangladesh Water Development Board's AI-Powered River Flood Alert System Evidence: Descriptive / self-reported Top 13% 80/100 Good practice Dhaka, Bangladesh —
femLINKpacific Women's Weather Watch — Community Women Leading Disaster Early Warning and Climate Response, Fiji (2004–present) Evidence: Descriptive / self-reported Top 44% 74/100 Good practice Suva, Fiji —
ML-Enhanced Vegetation Condition Forecasting — Kenya's National Drought Management Authority Early-Warning Extension Evidence: Observational / pre–post Top 35% 67/100 Good practice Nairobi, Kenya —
Southeast Asia's First Live AI Weather Forecasting System — DOST-PAGASA & ASTI (Philippines) Evidence: Descriptive / self-reported Top 50% 60/100 Good practice Quezon City, Philippines —
Combined Drought Index — Timor-Leste's AI-Assisted Anticipatory Action System for Agricultural Drought Evidence: Observational / pre–post Top 50% 60/100 Good practice Dili, Timor-Leste —
ShoShin AI Early Warning — Kenya's Sensor-Based Alert System for Lake Victoria Fish Kills Evidence: Observational / pre–post Top 66% 53/100 Good practice Kisumu, Kenya —
AI for Early Warnings for All — Malawi's Pilot to Integrate the 'Bris' AI Weather Model into National Forecasting Evidence: Observational / pre–post Top 75% 47/100 Good practice Blantyre, Malawi —
AI-Augmented Early Warning System — Mozambique's INGD/INAM Pilot for Cyclone and Flood Forecasting Evidence: Descriptive / self-reported Top 83% 40/100 Good practice Maputo, Mozambique —
NIHSA Annual Flood Outlook — Nigeria's AI-Assisted Flood Risk Forecasting Across 250+ Monitoring Stations Evidence: Descriptive / self-reported Top 83% 40/100 Good practice Abuja, Nigeria —
SAFE-RISCCS — Nepal's Early-Stage AI Landslide Early-Warning Pilot Evidence: Descriptive / self-reported Top 88% 33/100 Good practice Kathmandu, Nepal —
AI-Assisted 'Smart Dam' Pilot for Water-Flow Monitoring on Iraq's Kufa and Al-Abbasiya Regulators Evidence: Descriptive / self-reported Top 100% 7/100 Good practice Kufa, Iraq —