How AI Can Predict Natural Disasters and How Effective It Is: Global Perspectives and Opportunities for Vietnam

As global climate patterns grow increasingly erratic, natural disasters strike with greater frequency, intensity, and unpredictability than what traditional forecasting models can effectively capture. Early-warning capabilities have therefore become a critical lifeline for many nations. Rapid developments in artificial intelligence (AI) over the past few years have opened a new chapter in disaster prevention. Instead of relying solely on meteorological observations and classical computational models, many countries have begun using AI to predict storms, floods, droughts, wildfires, and sea-level rise, creating disaster-management ecosystems that are faster, smarter, and more accurate.

For Vietnam, the strategic recommendation from Pauline Tamesis—UN Resident Coordinator—emphasizes the importance of integrating AI into national disaster-risk management. She noted that Vietnam consistently ranks among the top 10 to 15 countries most affected by climate change according to the Global Climate Risk Index, meaning investment in advanced forecasting technologies is no longer optional but essential.

This article provides a comprehensive analysis of how AI predicts natural disasters, where it is currently being applied, the effectiveness achieved, expert perspectives, and what Vietnam can learn—especially in a year when the country faced one of its most intense storm seasons in three decades.

1. How AI Predicts Natural Disasters

The primary strength of AI lies in its ability to process massive volumes of data at extraordinary speed. Natural disasters—especially storms, floods, and droughts—are influenced by countless variables: sea-surface temperatures, humidity, wind speed, air pressure, the movement of atmospheric systems, river discharge, soil absorption, and many more. Humans and traditional models struggle to process such dynamic, multilayered datasets. AI, on the other hand, can ingest, analyze, and correlate these inputs into a cohesive predictive system.

AI-based disaster prediction typically operates through three major layers: data collection, predictive analysis, and actionable early-warning outputs. In the data-collection phase, information flows not only from wind sensors, meteorological satellites, and oceanographic models but also from IoT devices installed along coastlines, mountainous regions, drainage systems, and rivers. AI models then attempt to uncover hidden correlations among these complex variables—patterns the human eye could never fully recognize. From these patterns, AI can predict storm intensity, wind speeds, rainfall levels, peak flood timing, landslide risks, and wildfire spread.

Another vital capability is AI’s ability to update forecasts continuously using real-time data. When a storm suddenly changes course or intensifies, AI recalculates trajectories and hazards within minutes, offering warnings earlier by several hours or even tens of hours. This time difference can determine the success of large-scale evacuations and protective measures.

AI also excels at forecasting secondary disasters: post-storm flooding, landslides triggered by prolonged rainfall, or dam failure from excessive water pressure. When paired with satellite imagery, AI can identify structural weaknesses in infrastructure and highlight areas of urban overdevelopment at risk during heavy rainfall.

2. Countries Using AI for Disaster Prediction and Their Achievements

Several countries have implemented AI-based early-warning systems to varying degrees, and most have reported substantial improvements in accuracy and response readiness.

Japan – Global Benchmark for Precision Forecasting

Japan, frequently struck by typhoons, earthquakes, tsunamis, and volcanic activity, is among the earliest adopters of AI in forecasting heavy rainfall and flash floods. The Japan Meteorological Agency (JMA) uses AI to analyze cloud movement, sea-surface temperatures, and radar data, achieving up to 88% accuracy in predicting rainfall 10 minutes ahead. During Japan’s severe rainfall disaster in 2023, AI correctly identified high-risk landslide zones, enabling local authorities to evacuate 200,000 residents before major slides occurred.

India – AI Alerts Saving Thousands in Flood-Prone Regions

India, with its diverse climate and high disaster exposure, partnered with Google to deploy AI-based flood forecasting. The system integrates terrain data, river flow, and rainfall to predict water-level rise for each village or district. By 2024, Google Flood Hub had expanded to over 8,000 regions, providing early warnings to around 360 million people.

India’s Ministry of New and Renewable Energy reported that in major floods along the Brahmaputra and Ganges rivers, AI warnings arrived 12–24 hours earlier than traditional forecasts, reducing fatalities by nearly half.

United States – AI for Wildfire Management and Hurricane Tracking

The U.S., particularly California, struggles with historic wildfire seasons and increasingly powerful Atlantic hurricanes. NOAA’s AI-based Hurricane Analysis Forecast System improves track prediction accuracy by about 20% compared with previous models. Meanwhile, California’s Firecast AI platform analyzes environmental conditions to predict wildfire ignition points and spread patterns, helping fire departments deploy earlier and reducing the extent of destruction.

China – Real-Time Flood and Dike-Monitoring Systems

China has built AI-enhanced flood-warning systems for major rivers such as the Yangtze and Yellow Rivers. Thousands of cameras and IoT sensors along dikes stream continuous data into AI models to predict overflow or structural failures. This real-time surveillance significantly strengthens the nation’s flood-response capability during complex monsoon seasons.

3. The United Nations’ Recommendation and What It Means for Vietnam

During the Autumn Economic Forum 2025, held on November 26 in Ho Chi Minh City, Pauline Tamesis—UN Resident Coordinator with over 25 years of leadership experience in sustainable development—emphasized that Vietnam should prioritize AI-based early-warning systems. Her message is a strategic reminder: Vietnam’s vulnerability to climate extremes requires advanced forecasting technology.

In 2025, the East Sea experienced 15 storms and 5 tropical depressions, making it one of the stormiest years in three decades. Severe flooding and landslides across central and northern regions demonstrated how traditional forecasting models are reaching their limits under current climate volatility.

According to Pauline Tamesis, AI can help Vietnam:

• accelerate analysis of meteorological and hydrological data,
• deliver more detailed early warnings to specific localities,
• give communities more preparation time before disasters strike,
• support long-term land-use and infrastructure planning.

She also stressed that AI will be most effective if Vietnam invests simultaneously in renewable energy, education and workforce skills, and digital infrastructure. These three pillars create the foundation for technological progress.

4. What AI Can Bring to Vietnam

Vietnam’s geography—long coastline, densely populated deltas, mountainous regions prone to flash floods—makes it a prime candidate for AI-enhanced forecasting. If implemented effectively, AI could be transformative.

AI can predict flash floods and landslides in northern mountainous provinces like Lao Cai, Yen Bai, and Ha Giang by analyzing soil moisture, rainfall accumulation, and slope topography. This helps authorities issue evacuation orders earlier than ever.

In coastal provinces, AI can simulate numerous storm-landfall scenarios, revealing potential impact locations and arrival times with greater precision. This matters greatly when decisions must be made about evacuating hundreds of thousands of people within short windows of 24–48 hours.

Urban centers like Ho Chi Minh City and Hanoi could also benefit from AI-powered flood-forecasting models. By merging rainfall data, drainage-system capacity, and tidal variations, AI can predict street-level flooding, allowing traffic management and emergency teams to mobilize ahead of time.

AI also enables long-term urban and regional planning. Satellite-based AI assessments can reveal weak soil compositions, high-risk erosion zones, or areas with overly dense construction, guiding cities to avoid high-risk development patterns.

5. Expert Perspectives from Around the World

International experts view AI as a transformative force in climate-risk management. Dr. Michael Byrne of the University of Oxford believes AI is shifting humanity from reactive disaster response to proactive risk mitigation. Traditional climate models often have significant time lags, but AI dramatically shortens these delays.

Professor Rika Morimoto of the University of Tokyo points out that AI not only saves lives but also reduces economic losses. Japan spends billions after each major storm, yet AI-driven preparedness has lowered these costs substantially by enabling earlier interventions.

Daniel Murphy, senior advisor at the UNFCCC, emphasizes that AI is especially crucial for developing countries where the most climate-vulnerable populations are also the poorest. In his view, effective AI deployment can help close socio-economic gaps because early warnings protect the people who need them most.

6. What Vietnam Must Do to Capture This Opportunity

Vietnam has several advantages: a young population, rapid digital-transformation momentum, and expanding meteorological and hydrological datasets. Yet challenges remain: fragmented data, insufficient integration across sectors, and computational infrastructure that has not fully kept pace with modern AI demands.

For AI to succeed, Vietnam must unify datasets across ministries, expand sensor networks, improve satellite-data accessibility, strengthen national computing centers, and train specialists in AI-driven meteorology. These efforts align closely with the broader recommendations from Pauline Tamesis on renewable energy, education, and digital infrastructure. Once these foundations are strengthened, Vietnam can leverage AI not just in disaster warning but across numerous sectors of national development.

AI is ushering in a new era of disaster prediction.

AI is ushering in a new era of disaster prediction in which humanity can reduce risks through faster, more accurate, real-time insights. Countries like Japan, the U.S., China, and India have already demonstrated how AI saves lives and reduces economic damage. Vietnam—situated in one of the world’s most active storm regions—must not stay behind.

The United Nations’ recommendation is both a warning and a call to action: AI is essential if Vietnam wants to protect its people against escalating climate extremes. Coupled with strong investments in education, digital infrastructure, and clean energy, AI can help Vietnam build a safer, more resilient, and more sustainable future in an increasingly unpredictable world.