New technologies boost accuracy of disaster early warning systems

The rapid increase in the frequency, intensity, and extremity of natural hazards is creating an urgent need to modernize forecasting and early warning systems to make them faster, more accurate, and more intelligent.

This was a central theme at the workshop “New Technologies in Disaster Forecasting and Early Warning” held on the morning of March 18 in Hanoi. The event was co-organized by Viet Nam Agriculture and Nature Newspaper, the Viet Nam Meteorological and Hydrological Administration (VNMHA) and the Community-Based Disaster Prevention Fund, as part of activities marking World Water Day (March 22) and World Meteorological Day (March 23). It brought together representatives from ministries, local authorities, international organizations, scientists, businesses, and media agencies.

New technologies seen as a breakthrough

Speaking at the workshop, Deputy Minister of Agriculture and Environment Le Cong Thanh said that disasters in Viet Nam and globally are clearly intensifying due to the impacts of climate change. Extreme events such as powerful storms, localized heavy rainfall, flash floods, landslides, droughts, and saltwater intrusion are occurring more frequently and causing severe damage to livelihoods and socio-economic development.

Between 2021 and 2025, Viet Nam has been affected by an average of 10 to 12 storms and tropical depressions annually in the East Sea, along with hundreds of episodes of heavy rainfall and other extreme weather events. Over the past five years, disasters have left more than 1,500 people dead or missing and caused economic losses totaling hundreds of trillions of Vietnamese dong.

In addition to increasing in number, many types of disasters are becoming more localized, intense, and fast-evolving, while traditional forecasting methods are showing clear limitations. Accurate and timely warnings are therefore a critical tool for risk reduction, directly shaping the ability of authorities and communities to respond.

According to the deputy minister, new technologies represent a key breakthrough. These include AI, big data, remote sensing, high-resolution numerical forecasting models, smart sensor systems, and real-time data analytics platforms. Together, they can significantly shorten processing time, improve forecast accuracy, and support the development of more effective response scenarios.

Looking ahead, the Ministry of Agriculture and Environment plans to prioritize digital transformation in meteorology and hydrology, build large-scale data systems, and develop modern forecasting infrastructure. It also aims to strengthen data connectivity and sharing among government agencies, research institutions, businesses, and international partners.

At the same time, the ministry will continue modernizing observation networks, expanding automated monitoring stations, leveraging satellite data, and developing multi-hazard early warning platforms. Another key priority is ensuring that warnings reach the public more quickly and accessibly through digital platforms, mobile devices, and multi-channel communication systems.

“International cooperation and public-private partnerships must also be strengthened to mobilize resources for research and technology transfer,” Thanh emphasized.

From a community perspective, Dr. Cao Duc Phat, former Minister of Agriculture and Rural Development and Chairman of the Community-Based Disaster Prevention Fund, noted that the meteorological sector has made significant efforts to improve forecasting and early warning capacity, helping local authorities and communities take more proactive measures.

However, he stressed the need for more effective solutions to deliver forecasts and warnings “closer to and faster for communities.” The fund aims to mobilize social resources to support communities in strengthening preparedness and response capacity. “We hope to contribute to advancing the application of technical innovations, especially digital technologies and AI, to make disaster forecasting and warning increasingly effective,” he said.

AI improves storm forecast accuracy

From a technical standpoint, Dr. Nguyen Xuan Hien, Deputy Director of the National Center for Hydro-Meteorological Forecasting, said the sector is accelerating the adoption of new technologies to enhance forecasting capacity.

A key development is the integration of AI into storm and severe weather forecasting, which has improved short-term storm intensity predictions by 10 - 20%. AI also enables the automatic identification of storm position and intensity from satellite data with accuracy exceeding 90%.

Short-term warning systems for thunderstorms, tornadoes, and extreme rainfall are also being developed using multi-source data, including radar, satellite imagery, lightning detection, and automated monitoring stations, allowing alerts to be issued from 30 minutes to several hours in advance.

In addition, real-time monitoring and warning platforms for flash floods and landslides, down to the commune level, are gradually being deployed. Advanced hydrological and hydraulic models are also supporting forecasts of floods and saltwater intrusion across river basins.

Despite these advances, significant challenges remain. According to Hien, the accuracy of heavy rainfall forecasts is still limited, particularly at critical thresholds. Errors in predicting storm tracks and intensity remain considerable and have not improved significantly in recent years.

Observation systems are not yet fully synchronized, data integration remains incomplete, and key baseline datasets, such as real-time geological and topographical data, are still lacking. This makes it difficult to issue detailed, location-specific warnings for flash floods and landslides.

Other bottlenecks include shortages of high-quality human resources, as well as limitations in financial mechanisms and policy incentives for research and experimentation with new technologies.

In the coming years, the meteorological sector will focus on four main pillars: mastering core technologies (especially AI and ensemble forecasting models), developing real-time smart observation systems, enhancing big data processing capabilities, and improving the effectiveness of delivering warnings to the public.

Specific targets include reducing storm intensity forecast errors to below 7 m/s within a one- to three-day lead time, increasing the accuracy rate of rainfall forecasts to over 60% at key thresholds, and developing AI models capable of forecasting floods and identifying hazardous weather events two to five days in advance.

Experts agree that as natural disasters increasingly deviate from traditional patterns, technology, particularly AI and big data, will play a pivotal role. However, the effectiveness of these technologies will ultimately depend on robust data infrastructure, comprehensive observation systems, and supportive policy frameworks.