The ethical integration of artificial intelligence (AI) into early warning systems remains a challenge despite many advancements in related realms. AI needs to be deployed here responsibly, says Sanjay Srivastava, Chief, Disaster Risk Reduction, UN-ESCAP.
AI tools must uphold rigorous standards for data governance, bias mitigation, cybersecurity, and ethical integrity, Srivastava told business line. “Transparency in algorithms and clear accountability mechanisms are essential to building public trust in AI-generated alerts. False alarms or inaccurate warnings can undermine credibility and must be minimised through validation and testing.”
Substantial resources
AI-driven solutions often require substantial technical and financial resources, mostly unavailable in the very regions where early warnings are urgently needed. Energy-intensive nature of these models exacerbates this disparity.
Srivastava cited the case of the India Meteorological Department’s (IMD) Regional Specialised Meteorological Centre (RSMC) operating under the Panel on Tropical Cyclones (PTC), which monitors cyclones across the northern Indian Ocean from Oman to Myanmar.
In collaboration with 13 PTC member states, the RSMC is modernising Multi-Hazard Early Warning System (MHEWS) frameworks and integrating AI-based forecasting techniques, Srivastava said.
IMD’s initiative with AI
A key innovation is the use of AI to predict Tropical Cyclone Heat Potential (TCHP)- a crucial factor in forecasting cyclone intensity. Incorporating AI into regional cooperation frameworks enhances EW4All (Early Warnings for All initiative of the UN) efforts.
The 52nd session of the WMO/ESCAP Panel on Tropical Cyclones had discussed the role of AI in advancing the EW4All initiative by 2027. While progress is underway, vulnerable countries continue to suffer disproportionately.
Two key challenges
Two key challenges are: Even in countries with MHEWS) capacities, there are gaps among the four essential pillars: risk knowledge, forecasting and detection, warning dissemination, and timely response. Traditionally, floods and storms caused the greatest losses. However, extreme heat has now emerged as a leading cause of weather-related fatalities.
AI, a potential game changer
Technologies powering AI-enabled EW4All are advancing at a remarkable pace. Key innovations include segmentation algorithms, intelligent satellites capable of detecting image changes, drone-based mapping and sensing, digital twins, crowdsourced and automated image analysis, and multilingual natural language processing.
Drones as enablers
A notable development is the emergence of drone-enabled communication networks and fully automated drone swarms, which revolutionise real-time data collection and dissemination by integrating seamless connectivity into emergency response systems.
AI is poised to significantly scale the effectiveness of early warning systems in five critical ways. First, it enhances disaster risk knowledge by addressing data gaps, especially in underserved or data-scarce areas. For example, tools like FloodSENS use machine learning to reconstruct flood zones under cloud cover by combining digital elevation models with water flow algorithms.
Predictive analytics
Second, AI improves forecasting capabilities through advanced predictive analytics and real-time data. Third, it streamlines the dissemination of information by consolidating severe weather data and optimising alert delivery across multiple channels and languages. Fourth, AI enables tailored communication, customising alerts based on individual users’ location and language.
AI also supports scenario simulation, helping enhance emergency preparedness and enables responsive decision-making. A case in point is Google Research’s AI model, which predicts riverine flooding up to seven days in advance across more than 80 countries.
