IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v121y2025i2d10.1007_s11069-024-06878-6.html
   My bibliography  Save this article

Comparisons of filter, wrapper, and embedded feature selection for rockfall susceptibility prediction and mapping

Author

Listed:
  • Chengming Lei

    (China University of Geosciences)

  • Chunyan Liu

    (The Third Geological Brigade of Guangdong Geological Bureau)

  • Yunbin Zhang

    (The Third Geological Brigade of Guangdong Geological Bureau)

  • Jianmei Cheng

    (China University of Geosciences)

  • Ruirui Zhao

    (China University of Geosciences)

Abstract

The selection of influencing factors is very important for the rockfall susceptibility prediction (RSP). To improve the reliability of rockfall susceptibility prediction, three feature selection methods were used and compared to select reasonable influencing factors. The three feature selection methods are filter, wrapper, and embedded, respectively. Filter methods are represented by ReliefF and chi-square, wrapper methods are represented by genetic algorithm (GA) and binary particle swarm optimization (BPSO), and embedded methods are represented by L1-norm minimization learning (LML) and recursive features elimination (RFE). Taking Meizhou City, Guangdong Province, China as the research area, 21 factors are preliminarily selected to establish a rockfall susceptibility evaluation system. The above six feature selection methods are applied to optimize the combination of factors, and the contribution of each factor in different methods is analyzed. Then, based on the optimized factor combination, the random forest (RF) model is used to predict the rockfall susceptibility. Finally, the performance of the models is evaluated. The results show that the main influencing factors of the rockfall in Meizhou City are annual average rainfall (the importance is 0.130), distance to the road (0.109), and spring kernel density (0.094). The BPSO-RF model has the best performance for all the metrics with the area under the receiver operating characteristic curve (AUC), Accuracy (ACC), Recall (REC) and F1 Score (FS) of 0.891, 0.818, 0.805 and 0.822 respectively. Compared with the initial RF model, the AUC, ACC, REC and FS of the BPSO-RF model are improved by 2.7%, 4.5%, 4.5% and 4.4%, respectively. The model performance of wrapper methods represented by GA and BPSO is significantly better than that of filter and embedded methods. It can be inferred that the wrapper method is based on feature subset search and considers the mutual information between features, which makes the method better in removing redundant features and optimizing RSP.

Suggested Citation

  • Chengming Lei & Chunyan Liu & Yunbin Zhang & Jianmei Cheng & Ruirui Zhao, 2025. "Comparisons of filter, wrapper, and embedded feature selection for rockfall susceptibility prediction and mapping," 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(2), pages 1911-1943, January.
    • Handle: RePEc:spr:nathaz:v:121:y:2025:i:2:d:10.1007_s11069-024-06878-6
    DOI: 10.1007/s11069-024-06878-6
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-024-06878-6
    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/s11069-024-06878-6?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. Fan, Cheng & Xiao, Fu & Wang, Shengwei, 2014. "Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques," Applied Energy, Elsevier, vol. 127(C), pages 1-10.
    2. Marta Fernandez-Hernández & Carlos Paredes & Ricardo Castedo & Miguel Llorente & Rogelio la Vega-Panizo, 2012. "Rockfall detachment susceptibility map in El Hierro Island, Canary Islands, Spain," 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. 64(2), pages 1247-1271, November.
    Full references (including those not matched with items on IDEAS)

    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. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    2. Liangwen Yan & Fengfeng Qian & Wei Li, 2018. "Research on Key Parameters Operation Range of Central Air Conditioning Based on Binary K-Means and Apriori Algorithm," Energies, MDPI, vol. 12(1), pages 1-13, December.
    3. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    4. Cui, Can & Zhang, Xin & Cai, Wenjian, 2020. "An energy-saving oriented air balancing method for demand controlled ventilation systems with branch and black-box model," Applied Energy, Elsevier, vol. 264(C).
    5. Tomasz Szul & Krzysztof Nęcka & Stanisław Lis, 2021. "Application of the Takagi-Sugeno Fuzzy Modeling to Forecast Energy Efficiency in Real Buildings Undergoing Thermal Improvement," Energies, MDPI, vol. 14(7), pages 1-16, March.
    6. Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
    7. Ahmad, Muhammad Waseem & Mourshed, Monjur & Rezgui, Yacine, 2018. "Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression," Energy, Elsevier, vol. 164(C), pages 465-474.
    8. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    9. Molina-Solana, Miguel & Ros, María & Ruiz, M. Dolores & Gómez-Romero, Juan & Martin-Bautista, M.J., 2017. "Data science for building energy management: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 598-609.
    10. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    11. Kofi Afrifa Agyeman & Gyeonggak Kim & Hoonyeon Jo & Seunghyeon Park & Sekyung Han, 2020. "An Ensemble Stochastic Forecasting Framework for Variable Distributed Demand Loads," Energies, MDPI, vol. 13(10), pages 1-20, May.
    12. Robinson, Caleb & Dilkina, Bistra & Hubbs, Jeffrey & Zhang, Wenwen & Guhathakurta, Subhrajit & Brown, Marilyn A. & Pendyala, Ram M., 2017. "Machine learning approaches for estimating commercial building energy consumption," Applied Energy, Elsevier, vol. 208(C), pages 889-904.
    13. Yuwen You & Zhonghua Wang & Zhihao Liu & Chunmei Guo & Bin Yang, 2024. "Load Prediction of Regional Heat Exchange Station Based on Fuzzy Clustering Based on Fourier Distance and Convolutional Neural Network–Bidirectional Long Short-Term Memory Network," Energies, MDPI, vol. 17(16), pages 1-19, August.
    14. Hyunsoo Kim & Jiseok Jeong & Changwan Kim, 2022. "Daily Peak-Electricity-Demand Forecasting Based on Residual Long Short-Term Network," Mathematics, MDPI, vol. 10(23), pages 1-17, November.
    15. Alipour, Panteha & Mukherjee, Sayanti & Nateghi, Roshanak, 2019. "Assessing climate sensitivity of peak electricity load for resilient power systems planning and operation: A study applied to the Texas region," Energy, Elsevier, vol. 185(C), pages 1143-1153.
    16. Joanna Piotrowska-Woroniak & Tomasz Szul, 2022. "Application of a Model Based on Rough Set Theory (RST) to Estimate the Energy Efficiency of Public Buildings," Energies, MDPI, vol. 15(23), pages 1-13, November.
    17. Ahmad, Tanveer & Chen, Huanxin & Huang, Ronggeng & Yabin, Guo & Wang, Jiangyu & Shair, Jan & Azeem Akram, Hafiz Muhammad & Hassnain Mohsan, Syed Agha & Kazim, Muhammad, 2018. "Supervised based machine learning models for short, medium and long-term energy prediction in distinct building environment," Energy, Elsevier, vol. 158(C), pages 17-32.
    18. Dario Cottafava & Giulia Sonetti & Paolo Gambino & Andrea Tartaglino, 2018. "Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms," Energies, MDPI, vol. 11(5), pages 1-18, May.
    19. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    20. Wu, Da-Chun & Amini, Amin & Razban, Ali & Chen, Jie, 2018. "ARC algorithm: A novel approach to forecast and manage daily electrical maximum demand," Energy, Elsevier, vol. 154(C), pages 383-389.

    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:121:y:2025:i:2:d:10.1007_s11069-024-06878-6. 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.