The increasing frequency of extreme natural phenomena and the impact these have on societies and ecosystems have led to the development of increasingly precise and automated early warning models. Recently, both international research and European projects are investing in the use of artificial intelligence and remote sensing to create systems that not only detect risks but also anticipate their consequences with sufficient time to make effective decisions and save lives.
The early warning models They are becoming established as a key tool for reducing damage and optimizing resources in the face of earthquakes, forest fires, pests, and forest decline. Thanks to the application of new technologies, these systems are no longer limited to issuing general warnings, but now integrate real-time data, satellite images, physiological records, and sensors distributed throughout the territory.
Seismic early warning thanks to artificial intelligence
One of the most notable advances comes from the joint work between Chilean and British universities, which have implemented a seismic intensity predictive model Based on artificial intelligence. Developed by researchers from the University of Los Andes, the University of Chile, and the University of Exeter, this system is capable of predicting the expected intensity of an earthquake up to 30 or 40 seconds before the peak of the phenomenon occurs, a margin that can make the difference when evacuating vulnerable structures or halting dangerous industrial processes.
The system, named HEWFERS (Hybrid Earthquake Early Warning Framework for Estimating Response Spectra) uses advanced machine learning techniques to analyze the first few seconds recorded at accelerographic stations. With this information, it is able to estimate not only the affected area but also the actual stress that buildings and other infrastructure will face. This provides civil protection and emergency officials with a more solid basis for deciding what measures to take in each case.
The initiative proposes its future widespread implementation in countries with high seismic activity, such as Chile, leveraging the existing network of stations at the National Seismological Center. Furthermore, validation using data from real earthquakes—such as those that occurred in Japan—demonstrates its potential for adaptation to other international seismic contexts.
Protection of pine and oak forests using automated models
In the forestry sector, too, the idea of early warning is gaining ground. The European project TREAD, led by the Institute of Sustainable Agriculture of the CSIC, aims to develop a system capable of identifying forests at risk of mortality, especially pine and oak forests in the Mediterranean environment. This technology focuses on the early detection of pests and diseases, using thermal remote sensing sensors and the analysis of physiological variables to detect the first signs of deterioration in trees.
According to investigators, early detection is essential to implement precision forestry and mitigate the economic and ecological impacts of forest decline. TREAD is supported by the University of Córdoba and the Portuguese center CoLAB ForestWISE, in addition to the support of the European Forest Institute, which underscores its international relevance.
Beyond data collection, the project envisages the creation of a open database and online viewer that allows information to be shared between scientists, forest managers, and public officials. The idea is to scale the model to all types of ecosystems, integrating new species and adapting the response to changing climate challenges.
Challenges and opportunities of new predictive models
The use of early warning models poses scientific and technical challengesAmong them, the difficulty in understanding and modeling physiological changes in plants under stress and the need to adapt algorithms to highly variable environmental conditions. Furthermore, artificial intelligence systems must be continuously calibrated to avoid bias and improve their accuracy, especially in situations where available data may be limited or fragmented.
Despite these difficulties, the trend is clear: the integration of predictive analytics and big data technologies In risk management, it is transforming the way we address emergencies. Increasingly, these tools allow us to anticipate the potential magnitude of a disaster in a matter of seconds, facilitating a faster and more targeted response.
The development of early warning models for both earthquakes and forest health, demonstrates the importance of international collaboration and investment in applied researchThe advanced systems already being tested in Europe and Latin America represent a step forward in protecting critical infrastructure, natural ecosystems, and vulnerable communities from increasingly frequent and unpredictable threats.
