Analysis of earthquake forecasting using random forest

Abstract

The subject of forecasting earthquakes is an intriguing one to investigate. As a natural calamity, earthquakes continue to be devastating, not just to the economy but also to the lives of individuals. This gave rise to the concept of creating an early warning system against seismic catastrophes to minimize deaths. Researchers have been making earthquake forecasts and seismic hazard ratings of a location for a few years now. In this work, we attempt to forecast earthquakes before they occur using p-arrival data, which includes information on disaster arrival time and amplitude height from the arrival station. Several studies on earthquake prediction have been carried out so far and have developed and used the Random Forest method and one of the Machine Learning. According to, the process of predicting earthquakes has been studied for a long time, but there is still uncertainty due to the diversity and complexity of the earthquake phenomenon itself. According to, conducting a random forest prediction model to identify the structural safety status of buildings damaged by the earthquake is probabilistic. An earthquake's latitude, longitude, magnitude, and depth may be predicted using the random forest algorithm. A random forest with multioutput technique is employed, with variables being each station's recorded value and geographic position. This study's predictions were accurate to within 63 percent.

Analisis Hasil Prediksi Magnitudo Gempa Di Wilayah Kota Padang Menggunakan Teknik Random Forest

“…Decision tree atau pohon keputusan merupakan flowchart yang berbentuk seperti pohon yang memiliki sebuah root node (bagian akar) yang digunakan untuk mengumpulkan data (Setio et al, 2020). Langkah-langkah metode random forest (Budiman, 2021 Pembahasan Dalam penelitian menggunakan algoritma random forest, jumlah pohon sangat menentukan hasil output dari perhitungan. Maka dalam penelitian ini jumlah pohon yang akan dibangun adalah 100 pohon.…”