Accurate Low-Cost Earthquake Detection and Alarm System Based on WSN, ESP32 and Machine Learning - Hasanat, Mariam; Ayaydah, Lama (جامعة بوليتكنك فلسطين - هندسة حاسوب, 2024)
Earthquakes can occur suddenly and unexpectedly, causing widespread damage and loss of life. For example, the 2023 Syria-Turkey earthquake resulted in the death of over 50,000 people. Primary Wave (P-wave) and Secondary Wave (S-wave) are two types of seismic waves generated by earthquakes. P-wave is faster than S-wave, However, S-waves can do more damage than P- waves, and P-waves are used to detect the earthquake. Early earthquake warnings can help reduce these damages by giving people time to protect their lives. This leads us to propose a highly accurate, cost-effective, and accessible earthquake early warning system. The system utilizes a wireless sensor network to collect data from the ground using acceleration sensor ADXL335 and pass it to the ESP32 microcontroller to detect and predict earthquakes using a logistic regression machine learning model. The system also includes an alert mechanism using buzzers capable of notifying users of a near earthquake and providing audible alerts on mobile applications to increase speed and allow users sufficient time to take safety measures. Finally, the system uses a shaking table that significantly simulates earthquake conditions for evaluation and testing purposes. After picking 6000 earthquake and not-earthquake acceleration samples for x, y, and z from the shake table, these samples are divided and used to calculate the mean and standard deviation, each 100 samples together will give one vector of x, y, and z standard deviation values, and number of samples after calculating stand deviation is 43 per second, these sample will be used later to train and test the logistic regression model, that gives us a very high accuracy of 95.3%, which mean predict the earthquake in 0.5-1 sec. In the WSN, we implement star topology of two nodes, the 100% confidence of an earthquake happening depends on both nodes' values of alert, and the time arrival of alert to the mobile application depends on the internet latency, the total time needed from detecting the earthquake to mobile alerting is 7.5-12.5sec. mobile application is designed in a way that achieves easy and simple user interaction.
no of pages 58, هندسة حاسوب 8/2024
Keywords: Machine Learning; WSN; ESP32; Earthquake Detection and Alarm Systems