Hanoi (VNA) – Artificial intelligence (AI) has emerged as a vital tool to enhance disaster prediction, reduce damage, and support timely response efforts.
Early warning is the most effective measure for mitigating damage caused by weather-related and hydrological disasters. Studies indicate that early alerts issued at least 24 hours in advance can reduce economic losses by approximately 30%.
As climate change continues to intensify, leading to more frequent and severe storms, heavy rains, floods, and other extreme weather events, accurate and prompt early warning has become more crucial than ever.
Vietnam's meteorological and hydrological sector has been actively integrating AI, big data, and digital transformation into its forecasting operations.
Since the beginning of this year, the National Centre for Hydro-Meteorological Forecasting (NHCF) under the Ministry of Agriculture and Environment has implemented AI in several stages of its forecasting.
Specifically, machine learning algorithms trained on radar data, satellite imagery, and automated observations help generate short-term rainfall forecasts with high detail and rapid response.
For storm recognition and intensity assessment over the East Sea, AI-powered systems analyse meteorological satellite images to identify cyclone centres, evaluate storm strength, and track development trends. These systems, still under refinement and expansion, are integrated into operational forecasting workflows to provide continuous weather monitoring. This technology enables authorities to issue early warnings and take proactive measures to safeguard lives and property.
Mai Van Khiem, Director of the National Centre for Hydro-Meteorological Forecasting, stated that during the current typhoon and flood seasons, the application of AI has led to higher forecasting accuracy compared to traditional methods. For example, the forecasted position of a cyclone's centre within a 24-hour window now has an error margin of about 90-110 km, aligning with regional standards. AI assists in probabilistic assessments and uncertainty analysis, supporting decision-making for disaster preparedness and response.
In heavy rainfall forecasting, models like the Weather Research and Forecasting (WRF) system and regional ensemble approaches provide satisfactory results for widespread rain events. However, short-duration, localised downpours, especially in complex terrains with interacting weather systems, are difficult to predict. Combined radar, satellite data, ensemble algorithms, and nowcasting techniques have improved storm and lightning alerts, allowing warnings to be issued 30 minutes to three hours in advance in critical areas.
Despite these advances, Vietnam’s forecasting capacity still lags behind countries like Japan, China, and the Republic of Korea, which have more mature observation systems and technological infrastructure. Limited budgets, underdeveloped technological infrastructure, and a shortage of high-skilled experts constrain further AI adoption. Current observation stations are sparse, and processing AI models requires high-performance computing chips at significant cost.
Given the escalating impacts of climate change and the rise of extreme weather events, the modernisation of meteorological and hydrological services is imperative. Developing a comprehensive, high-precision early warning system will act as the nation's first line of defence against natural disasters, protecting both citizens and economic stability.
Khiem emphasised that in the coming years, Vietnam’s meteorological sector must fully implement a comprehensive modernisation plan, focusing on enhancing forecasting and warning capacities to serve disaster mitigation and sustainable development. Central to this strategy is the realisation of Resolution No. 57-NQ/TW, promoting breakthroughs in science and technology, innovation, and digital transformation.
Developing a smart, multi-scale prediction system will not only improve weather forecasting reliability but also facilitate more effective early warnings for a variety of natural hazards. This effort includes strengthening the capacity to forecast extreme weather phenomena, establishing multi-hazard early warning systems, and training a skilled workforce, especially among young professionals.
Furthermore, there will be an increased focus on community outreach and awareness campaigns to highlight the vital role of meteorological and hydrological information in reducing disaster risk. International cooperation will also be intensified to access technical and technological support, as well as to share knowledge and training resources./.
