TY - GEN
T1 - Using Machine Learning Models for Earthquake Magnitude Prediction in California, Japan, and Israel
AU - Novick, Deborah
AU - Last, Mark
N1 - Publisher Copyright: © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023/1/1
Y1 - 2023/1/1
N2 - This study aims at predicting whether an earthquake of magnitude greater than the regional median of maximum yearly magnitudes will occur during the next year. Prediction is performed by training various machine learning algorithms, such as AdaBoost, XGBoost, Random Forest, Logistic Regression, and Info-Fuzzy Network. The models are induced using a combination of seismic indicators used in the earthquake literature as well as various time-series features, such as features based on the moving averages of the number of earthquakes in each area, features that record the number of events above and below the mean in a time period, and features based on lagged values of the mean and median magnitude. Feature selection is performed using a forward search algorithm that chooses the most effective features for prediction. The models are trained and evaluated using earthquake catalog data obtained for California, Japan, and Israel. In addition, models trained on either California or Japan datasets are evaluated using the remaining data. Models trained on Japan data achieve AUC scores up to 0.825; models trained on California data achieve AUC scores up to 0.738; and models trained on Israel data achieve AUC scores up to 0.710.
AB - This study aims at predicting whether an earthquake of magnitude greater than the regional median of maximum yearly magnitudes will occur during the next year. Prediction is performed by training various machine learning algorithms, such as AdaBoost, XGBoost, Random Forest, Logistic Regression, and Info-Fuzzy Network. The models are induced using a combination of seismic indicators used in the earthquake literature as well as various time-series features, such as features based on the moving averages of the number of earthquakes in each area, features that record the number of events above and below the mean in a time period, and features based on lagged values of the mean and median magnitude. Feature selection is performed using a forward search algorithm that chooses the most effective features for prediction. The models are trained and evaluated using earthquake catalog data obtained for California, Japan, and Israel. In addition, models trained on either California or Japan datasets are evaluated using the remaining data. Models trained on Japan data achieve AUC scores up to 0.825; models trained on California data achieve AUC scores up to 0.738; and models trained on Israel data achieve AUC scores up to 0.710.
KW - Classification models
KW - Clustering analysis
KW - Earthquake prediction
KW - Seismicity indicators
UR - http://www.scopus.com/inward/record.url?scp=85164965545&partnerID=8YFLogxK
U2 - https://doi.org/10.1007/978-3-031-34671-2_11
DO - https://doi.org/10.1007/978-3-031-34671-2_11
M3 - Conference contribution
SN - 9783031346705
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 151
EP - 169
BT - Cyber Security, Cryptology, and Machine Learning - 7th International Symposium, CSCML 2023, Proceedings
A2 - Dolev, Shlomi
A2 - Gudes, Ehud
A2 - Paillier, Pascal
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2023
Y2 - 29 June 2023 through 30 June 2023
ER -
