IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v120y2024i2d10.1007_s11069-023-06252-y.html
   My bibliography  Save this article

Model selection for prediction of strong ground motion peaks in Türkiye

Author

Listed:
  • Gökhan Altay

    (Osmaniye Korkut Ata University)

  • Cafer Kayadelen

    (Osmaniye Korkut Ata University)

  • Mehmet Kara

    (Osmaniye Korkut Ata University)

Abstract

This study focuses on the model selection of peak ground acceleration (PGA), peak ground velocity (PGV), and peak ground displacement (PGD) prediction utilizing strong motion data of earthquakes that occurred in Türkiye. An inventive gradient boosted model (GBM) predicted the PGA, PGV, and PGD using a database obtained from The Ministry of Interior Disaster and Emergency Management Presidency of Türkiye (AFAD). Alternative classification and decision tree models like K-Star algorithm, Sequential Minimal Optimization Regression (SMOreg), Random Forest (RF), and K-Nearest Neighbors (K-NN) were utilized to make comparisons with the results obtained from GBM. Furthermore, this comparison also includes the PGA prediction outcomes obtained through the use of the artificial neural network (ANN) technique. Earthquake magnitude (Mw), epicentral distance (Repi), focal depth (FD), and mean shear-wave velocity from the surface to a depth of 30 m (Vs30) were considered as independent input parameters. Akaike information criterion (AIC) and Schwartz Bayesian criterion (SBC) were employed to evaluate the prediction performance of all models. The results obtained from the GBM model were found to be better regarding PGA prediction than the other models. These results were revealed models to be a promising approach for the prediction of PGA, and capable of representing the complex relationship between predicted and input parameters.

Suggested Citation

  • Gökhan Altay & Cafer Kayadelen & Mehmet Kara, 2024. "Model selection for prediction of strong ground motion peaks in Türkiye," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(2), pages 1443-1461, January.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:2:d:10.1007_s11069-023-06252-y
    DOI: 10.1007/s11069-023-06252-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-023-06252-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11069-023-06252-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Friedman, Jerome H., 2002. "Stochastic gradient boosting," Computational Statistics & Data Analysis, Elsevier, vol. 38(4), pages 367-378, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Samiya Akhtar & Muhammad Mohsin & Zulfiqar Ali, 2025. "Modeling of ground motion data to assess the seismic features for monitoring the seismic activity," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 121(5), pages 6211-6231, March.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bissan Ghaddar & Ignacio Gómez-Casares & Julio González-Díaz & Brais González-Rodríguez & Beatriz Pateiro-López & Sofía Rodríguez-Ballesteros, 2023. "Learning for Spatial Branching: An Algorithm Selection Approach," INFORMS Journal on Computing, INFORMS, vol. 35(5), pages 1024-1043, September.
    2. Nahushananda Chakravarthy H G & Karthik M Seenappa & Sujay Raghavendra Naganna & Dayananda Pruthviraja, 2023. "Machine Learning Models for the Prediction of the Compressive Strength of Self-Compacting Concrete Incorporating Incinerated Bio-Medical Waste Ash," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    3. Wen, Shaoting & Buyukada, Musa & Evrendilek, Fatih & Liu, Jingyong, 2020. "Uncertainty and sensitivity analyses of co-combustion/pyrolysis of textile dyeing sludge and incense sticks: Regression and machine-learning models," Renewable Energy, Elsevier, vol. 151(C), pages 463-474.
    4. Spiliotis, Evangelos & Makridakis, Spyros & Kaltsounis, Anastasios & Assimakopoulos, Vassilios, 2021. "Product sales probabilistic forecasting: An empirical evaluation using the M5 competition data," International Journal of Production Economics, Elsevier, vol. 240(C).
    5. Kusiak, Andrew & Zheng, Haiyang & Song, Zhe, 2009. "On-line monitoring of power curves," Renewable Energy, Elsevier, vol. 34(6), pages 1487-1493.
    6. Zhu, Siying & Zhu, Feng, 2019. "Cycling comfort evaluation with instrumented probe bicycle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 217-231.
    7. Cao, Jason & Tao, Tao, 2025. "Can an identified environmental correlate of car ownership serve as a practical planning tool?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 191(C).
    8. Dursun Delen & Hamed M. Zolbanin & Durand Crosby & David Wright, 2021. "To imprison or not to imprison: an analytics model for drug courts," Annals of Operations Research, Springer, vol. 303(1), pages 101-124, August.
    9. Doruk Cengiz & Arindrajit Dube & Attila S. Lindner & David Zentler-Munro, 2021. "Seeing Beyond the Trees: Using Machine Learning to Estimate the Impact of Minimum Wages on Labor Market Outcomes," NBER Working Papers 28399, National Bureau of Economic Research, Inc.
    10. Zhou, Jing & Li, Wei & Wang, Jiaxin & Ding, Shuai & Xia, Chengyi, 2019. "Default prediction in P2P lending from high-dimensional data based on machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    11. Lu, Yingjie & Li, Tao & Hu, Hui & Zeng, Xuemei, 2023. "Short-term prediction of reference crop evapotranspiration based on machine learning with different decomposition methods in arid areas of China," Agricultural Water Management, Elsevier, vol. 279(C).
    12. Bohdan M. Pavlyshenko, 2019. "Machine-Learning Models for Sales Time Series Forecasting," Data, MDPI, vol. 4(1), pages 1-11, January.
    13. Matthias Bogaert & Lex Delaere, 2023. "Ensemble Methods in Customer Churn Prediction: A Comparative Analysis of the State-of-the-Art," Mathematics, MDPI, vol. 11(5), pages 1-28, February.
    14. Jason R. W. Merrick & Claire A. Dorsey & Bo Wang & Martha Grabowski & John R. Harrald, 2022. "Measuring Prediction Accuracy in a Maritime Accident Warning System," Production and Operations Management, Production and Operations Management Society, vol. 31(2), pages 819-827, February.
    15. Buzna, Luboš & De Falco, Pasquale & Ferruzzi, Gabriella & Khormali, Shahab & Proto, Daniela & Refa, Nazir & Straka, Milan & van der Poel, Gijs, 2021. "An ensemble methodology for hierarchical probabilistic electric vehicle load forecasting at regular charging stations," Applied Energy, Elsevier, vol. 283(C).
    16. Adler, Werner & Lausen, Berthold, 2009. "Bootstrap estimated true and false positive rates and ROC curve," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 718-729, January.
    17. Döpke, Jörg & Fritsche, Ulrich & Pierdzioch, Christian, 2017. "Predicting recessions with boosted regression trees," International Journal of Forecasting, Elsevier, vol. 33(4), pages 745-759.
    18. Andrea Sciandra & Alessio Surian & Livio Finos, 2021. "Supervised Machine Learning Methods to Disclose Action and Information in “U.N. 2030 Agenda” Social Media Data," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(2), pages 689-699, August.
    19. Mirosław Parol & Paweł Piotrowski & Piotr Kapler & Mariusz Piotrowski, 2021. "Forecasting of 10-Second Power Demand of Highly Variable Loads for Microgrid Operation Control," Energies, MDPI, vol. 14(5), pages 1-29, February.
    20. Tsao, Yu-Chung & Chen, Yu-Kai & Chiu, Shih-Hao & Lu, Jye-Chyi & Vu, Thuy-Linh, 2022. "An innovative demand forecasting approach for the server industry," Technovation, Elsevier, vol. 110(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:nathaz:v:120:y:2024:i:2:d:10.1007_s11069-023-06252-y. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.