Using artificial intelligence to fast-track achieving the Sendai targets

The Sendai Framework for Disaster Risk Reduction (2015-2030) marked a pivotal paradigm shift in global disaster management, moving the focus from merely reacting to disasters to proactively managing underlying disaster risks. 

This fundamental change emphasises anticipating future hazards and building systemic resilience through prevention and mitigation efforts across all sectors. In pursuing the framework's seven global targets, recent advancements in artificial intelligence (AI) offer transformative potential to accelerate this proactive approach.

Disaster Risk Reduction (DRR) is fundamentally a decision problem matter of decision-making under uncertainty: When to act, where to act, and how much to invest-before losses become irreversible. Climate change has turned this problem from episodic to continuous, with compounding and cascading risks across space and time. AI is now emerging not merely as a tool for faster computation, but as a new decision infrastructure for prevention and preparedness.

What makes this moment distinctive is not one technology, but the convergence of image intelligence, language intelligence, and agent-based decision intelligence, all operating across the disaster risk value chain.

1. Image intelligence: seeing risk before it becomes disaster

At the foundation of AI-enabled DRR lies image analysis, powered by deep learning and computer vision. Earth-observation satellites, drones, CCTV networks, and even smartphones generate petabytes of visual data-far beyond human interpretability.

AI transforms this flood of pixels into early signals of risk:

• Prevention: AI models detect subtle land deformation, glacier instability, shoreline erosion, forest stress, or informal settlement expansion-revealing latent risk long before a hazard manifests.
• Preparedness: Real-time image streams enable rapid identification of blocked drainage, rising river levels, heat-stress hotspots, or fire spread patterns, supporting anticipatory action rather than reactive response.

Crucially, image-based AI shifts DRR from event detection to risk trajectory monitoring, allowing decision-makers to see how vulnerability and exposure evolve under climate stress.

2. Large language models: from data to decisions

While image AI answers, "What is happening?," Large Language Models (LLMs) address a deeper bottleneck in DRR: sense-making and coordination.

Disaster risk governance is fragmented across institutions, mandates, and scales. LLMs act as cognitive bridges, enabling:

• Knowledge synthesis LLMs can rapidly integrate scientific reports, hazard bulletins, historical disaster records, and policy frameworks into actionable insights tailored for specific users-district officials, health workers, or infrastructure planners.
• Decision support and advisories By translating probabilistic forecasts into context-aware narratives ("what this heatwave means for outdoor workers in coastal districts"), LLMs close the long-standing gap between climate science and last-mile action.
• Institutional memory and continuity In many countries, disaster management suffers from staff turnover and loss of tacit knowledge. LLMs can function as living repositories of standard operating procedures, lessons learned, and adaptive practices.

In effect, LLMs are becoming decision companions, not replacing judgment but enhancing human capacity to reason under complexity and time pressure.

3. Agent-based and optimization models: choosing the best path forward

The most transformative leap comes when AI moves from prediction to prescription.

Agent-based models and AI-driven optimization systems simulate the behavior of households, markets, infrastructure networks, and institutions under stress. When coupled with real-time data, they allow policymakers to test "what-f" scenarios:

• Where should limited flood defenses be built to minimize long-term losses?
• How do evacuation strategies change when transport, health, and power systems interact?
• What is the optimal mix of early warnings, cash transfers, and ecosystem restoration to reduce heat mortality?

These models do not offer a single "correct" answer. Instead, they reveal trade-offs, distributional impacts, and unintended consequences-supporting transparent, evidence-based choices in prevention and preparedness planning.

4. AI as a prevention engine, not a response tool

The true value of AI in DRR lies upstream. By integrating image intelligence, language intelligence, and agent-based decision systems, AI enables a shift:

• From disaster management to risk governance
• From post-event relief to pre-event investment
• From static plans to adaptive, learning systems

When embedded into early-warning systems, urban planning, infrastructure design, agriculture advisories, and social protection, AI becomes a force multiplier for resilience-reducing losses before they occur rather than merely documenting them after the fact.

What comes after AI? The next wave of the revolution

If today's AI is about learning from data, the next wave will be about reasoning with the world.

Several frontier shifts are already emerging:

1. Hybrid Intelligence Systems

Future systems will tightly integrate machine intelligence with human judgment, indigenous knowledge, and institutional norms. Rather than replacing experts, AI will negotiate decisions with them-explicitly accounting for ethics, equity, and political feasibility.

2. Autonomous risk governance agents

We are moving toward semi-autonomous agents that can continuously monitor risk thresholds, trigger anticipatory finance, adjust warnings, and coordinate across agencies -under human oversight. DRR will become continuous and adaptive, not seasonal or event-based.

3. Causal and counterfactual AI

Beyond pattern recognition, next-generation models will ask: Why did this risk emerge? And what would have happened if we had acted earlier? This will fundamentally change how adaptation investments are evaluated and justified.

4. Planetary-scale intelligence

As teleconnections increasingly shape climate risks-from the Arctic to the Third Pole, from oceans to cities-AI systems will operate at a planetary scale, integrating atmospheric, cryospheric, ecological, and socio-economic signals into unified risk intelligence.

Several countries have already demonstrated the integration of AI in their specific contexts. In the Netherlands, for example, AI-driven flood models integrate satellite data, river dynamics, and infrastructure information to assess flood risk and guide spatial planning decisions continuously. In Japan, AI-powered systems analyse seismic, meteorological, and infrastructure data to support real-time decision-making during earthquakes, typhoons, and floods. Another quiet revolution is underway in Bangladesh, one of the world's most disaster-prone countries. AI-supported flood forecasting and early warning systems now provide location-specific alerts several days in advance, allowing communities to evacuate livestock, protect assets, and activate social safety net. Combined with anticipatory financing mechanisms, these systems have dramatically reduced cyclone-related mortality over the past two decades. The lesson is clear: AI delivers results when it is embedded into preparedness and social protection systems, not treated as a stand-alone innovation.

A key takeaway

AI is not the end of the DRR transformation-it is the beginning. The deeper revolution lies in how societies learn, decide, and act collectively in the face of uncertainty.

In a warming world where extremes are becoming the norm, the question is no longer whether we can predict disasters, but whether we can govern risk intelligently. AI, and what comes after it, offers us that possibility-if we choose to deploy it not just for efficiency, but for foresight, fairness, and resilience.

Dr. Sanjay K. Srivastava is a senior expert in disaster risk reduction, science-policy integration, and technology for resilience, currently a Chair Professor at NIAS and Adjunct Faculty at KMC/MAHE. He spent over a decade at UNESCAP as Chief of Disaster Risk Reduction, leading flagship initiatives such as the Asia-Pacific Disaster Report and advancing regional and national risk and resilience strategies, following earlier leadership roles at UNESCAP, SAARC, and ISRO. An award-winning scholar with over 150 publications, he holds a PhD in Remote Sensing and has additional training in digital transformation from UC Berkeley.