Optimizing earthquake damage prediction using particle swarm optimization-based feature selection

Nurul Anisa Sri Winarsih, Ricardus Anggi Pramunendar, Guruh Fajar Shidik, Budi Widjajanto, Muhammad Syaifur Rohman, Danny Oka Ratmana

Abstract


Earthquakes have destroyed the economy and killed many people in many countries. Emergency response actions immediately after an earthquake significantly reduce economic losses and save lives, so accurate earthquake damage predictions are needed. This research looks at how machine learning (ML) techniques are used to predict damage from earthquakes. The ML algorithms used are k-nearest neighbors (KNN), decision tree (DT), random forest (RF), and Naïve Bayes (NB). Feature selection is necessary, it needs to select the most relevant features from big data. One of the most commonly used algorithms to optimize ML is particle swarm optimization (PSO). PSO is also suitable for feature selection. This research compares various of PSO. Based on research, the RF algorithm with Phasor PSO has the highest fitness score. This process succeeded in reducing features from 38 features to 14 features. Based on the process after feature selection, it was found that the KNN, DT, and RF algorithms had improved. RF obtained the best accuracy, namely 72.989%. The processing time in DT, RF, and NB is faster than before. In conclusion, the ML algorithm can be combined with PSO feature selection to create a classification model that provides better performance than without feature selection.

Keywords


Earthquake damage prediction; Feature selection; Machine learning; Particle swarm optimization; Phasor particle swarm optimization

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DOI: https://doi.org/10.11591/eei.v14i1.8421

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Bulletin of EEI Stats

Bulletin of Electrical Engineering and Informatics (BEEI)
ISSN: 2089-3191, e-ISSN: 2302-9285
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).