Development of Advanced Tsunami Detection Software for Early Warning Systems Using Real-Time Data and Machine Learning

This study was carried out along the southern coastline of West Sumatra, Indonesia, a region highly vulnerable to tsunamis due to its proximity to the Mentawai Megathrust, a seismically active fault line. The area’s frequent seismic activity and history of destructive tsunamis made it an ideal location for testing the effectiveness of an advanced tsunami detection software. The primary goal of the study was to enhance the speed and accuracy of tsunami detection by integrating real-time data from various sources, including local seismic stations, ocean buoys, and satellite imagery. Machine learning algorithms, trained on historical tsunami events in the region, were employed to identify tsunami signatures and potential threats with greater precision. By using data specific to West Sumatra, the system was designed to account for local environmental and geological factors, making the detection more tailored and reliable. The software was also equipped with predictive modeling to forecast potential tsunami paths and their impact on coastal communities, enabling authorities to implement more timely and effective evacuation measures. Testing the software with both simulated and real-world data from the region demonstrated substantial improvements in detection time and accuracy compared to existing systems. This localized approach to tsunami detection not only offers better protection for the communities along the West Sumatra coastline but also serves as a model for improving early warning systems in other high-risk regions. Ultimately, this development represents a significant step forward in tsunami disaster preparedness, contributing to more effective risk mitigation and saving lives in vulnerable areas.