FMIPA UI Doctoral Researcher Develops a Hybrid Approach to Accelerate Earthquake Early Warning

Depok, July 22, 2026 — Efforts to improve public safety against earthquake risks continue to advance through science- and technology-based innovations. One such advancement was demonstrated by Kadnan, a researcher from FMIPA Universitas Indonesia, who successfully developed an earthquake early warning system using a hybrid approach that combines artificial intelligence and seismic physics.

In his doctoral dissertation, Kadnan developed a system capable of “reading” early earthquake signals within seconds. The system utilizes P-waves (primary waves)—the first seismic waves to arrive—to predict the intensity of shaking that will be experienced when the main S-waves (secondary waves) arrive.

In contrast to conventional approaches, this innovation integrates the strengths of machine learning in recognizing data patterns with the precision of earthquake physics analysis. The result is a system that is not only fast, but also accurate and scientifically sound.

The model was developed using thousands of earthquake records from western Java and demonstrated a high level of accuracy, reaching more than 92 percent. Testing on real cases, such as the 2009 Tasikmalaya earthquake and the 2022 Cianjur earthquake, showed that the system was able to provide predictions that closely matched actual field conditions.

Another aspect of this research is the concept on-site warning, where a monitoring station can independently generate alerts without having to wait for processing from the center. This approach is considered capable of reducing delays (latency) which has been a challenge in network-based systems.latency) yang selama ini menjadi tantangan dalam sistem berbasis jaringan.

Technically, this system also optimizes the initial wave arrival detection process using the method Initial Power of the P-wave (IPP) which has proven to be more stable in noisy data conditions. Accuracy in detecting this early phase is the main foundation for ensuring the overall reliability of the early warning system.

In addition, the developed prediction model utilizes key parameters of the P wave within a very short time window, namely the first three seconds after the event. This strategy allows the system to maintain an optimal balance between response speed (lead time) and the accuracy of the shock strength estimation (PGA).lead time) dan akurasi estimasi kekuatan guncangan (PGA).

“The main goal of this system is to save time—because in an earthquake, a few seconds can save many lives,” Kadnan said.

From an implementation perspective, this approach has significant potential for application in areas with uneven sensor coverage as well as in critical infrastructure requiring independent warning systems, such as power plants, high-speed rail networks, gas pipelines, hospitals, and other strategic public facilities in Bandung, Bogor, and Jakarta.

With western Java facing high seismic risks due to subduction activity, along with its high population density and concentration of vital infrastructure, this innovation is highly relevant. The integration of this hybrid approach is expected to strengthen the national earthquake early warning system, which currently remains largely dominated by centralized network-based approaches.

Furthermore, the findings of this research open opportunities for the development of next-generation earthquake early warning systems that are adaptive, scalable, and capable of being integrated with various technological platforms, including emergency communication systems and automated disaster response mechanisms.

In the future, this hybrid approach has the potential to serve as a foundation for developing more adaptive, faster, and more reliable earthquake early warning systems in Indonesia, while also strengthening science-based disaster mitigation efforts to protect communities more effectively.