IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i20p14928-d1260818.html
   My bibliography  Save this article

A Comparison of Machine Learning Models for Predicting Flood Susceptibility Based on the Enhanced NHAND Method

Author

Listed:
  • Caisu Meng

    (School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China)

  • Hailiang Jin

    (School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454003, China)

Abstract

A flood is a common and highly destructive natural disaster. Recently, machine learning methods have been widely used in flood susceptibility analysis. This paper proposes a NHAND (New Height Above the Nearest Drainage) model as a framework to evaluate the effectiveness of both individual learners and ensemble models in addressing intricate flood-related challenges. The evaluation process encompasses critical dimensions such as prediction accuracy, model training duration, and stability. Research findings reveal that, compared to Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Lasso, Random Forest (RF), and Extreme Gradient Boosting (XGBoost), Stacked Generalization (Stacking) outperforms in terms of predictive accuracy and stability. Meanwhile, XGBoost exhibits notable efficiency in terms of training duration. Additionally, the Shapley Additive Explanations (SHAP) method is employed to explain the predictions made by the XGBoost.

Suggested Citation

  • Caisu Meng & Hailiang Jin, 2023. "A Comparison of Machine Learning Models for Predicting Flood Susceptibility Based on the Enhanced NHAND Method," Sustainability, MDPI, vol. 15(20), pages 1-22, October.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:20:p:14928-:d:1260818
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/20/14928/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/20/14928/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhouyayan Li & Jerry Mount & Ibrahim Demir, 2022. "Accounting for uncertainty in real-time flood inundation mapping using HAND model: Iowa case study," 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. 112(1), pages 977-1004, May.
    2. B. Tellman & J. A. Sullivan & C. Kuhn & A. J. Kettner & C. S. Doyle & G. R. Brakenridge & T. A. Erickson & D. A. Slayback, 2021. "Satellite imaging reveals increased proportion of population exposed to floods," Nature, Nature, vol. 596(7870), pages 80-86, August.
    3. Beibei Liu & Chaowei Xu & Jiashuai Yang & Sen Lin & Xi Wang, 2022. "Effect of Land Use and Drainage System Changes on Urban Flood Spatial Distribution in Handan City: A Case Study," Sustainability, MDPI, vol. 14(21), pages 1-18, November.
    4. Hongchao Zhang & Tengteng Zhu, 2022. "Stacking Model for Photovoltaic-Power-Generation Prediction," Sustainability, MDPI, vol. 14(9), pages 1-16, May.
    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. Sean Fox & Felix Agyemang & Laurence Hawker & Jeffrey Neal, 2024. "Integrating social vulnerability into high-resolution global flood risk mapping," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    2. Caroline Taylor & Tom R. Robinson & Stuart Dunning & J. Rachel Carr & Matthew Westoby, 2023. "Glacial lake outburst floods threaten millions globally," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    3. Cheng He & Yixiang Zhu & Lu Zhou & Jovine Bachwenkizi & Alexandra Schneider & Renjie Chen & Haidong Kan, 2024. "Flood exposure and pregnancy loss in 33 developing countries," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    4. Jeffrey D. Michler & Dewan Abdullah Al Rafi & Jonathan Giezendanner & Anna Josephson & Valerien O. Pede & Elizabeth Tellman, 2024. "Impact Evaluations in Data Poor Settings: The Case of Stress-Tolerant Rice Varieties in Bangladesh," Papers 2409.02201, arXiv.org.
    5. Hongshi Xu & Kui Xu & Tianye Wang & Wanjie Xue, 2022. "Investigating Flood Risks of Rainfall and Storm Tides Affected by the Parameter Estimation Coupling Bivariate Statistics and Hydrodynamic Models in the Coastal City," IJERPH, MDPI, vol. 19(19), pages 1-18, October.
    6. Marcel Henkel, Eunjee Kwon, Pierre Magontier, 2022. "The Unintended Consequences of Post-Disaster Policies for Spatial Sorting," Diskussionsschriften credresearchpaper37, Universitaet Bern, Departement Volkswirtschaft - CRED.
    7. Mo Wang & Xiaoping Fu & Dongqing Zhang & Siwei Lou & Jianjun Li & Furong Chen & Shan Li & Soon Keat Tan, 2023. "Urban agglomeration waterlogging hazard exposure assessment based on an integrated Naive Bayes classifier and complex network analysis," 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. 118(3), pages 2173-2197, September.
    8. Pallavi Tomar & Suraj Kumar Singh & Shruti Kanga & Gowhar Meraj & Nikola Kranjčić & Bojan Đurin & Amitanshu Pattanaik, 2021. "GIS-Based Urban Flood Risk Assessment and Management—A Case Study of Delhi National Capital Territory (NCT), India," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    9. Yuanyuan Yang & Wenhui Zhang & Zhe Liu & Dengfeng Liu & Qiang Huang & Jun Xia, 2023. "Coupling a Distributed Time Variant Gain Model into a Storm Water Management Model to Simulate Runoffs in a Sponge City," Sustainability, MDPI, vol. 15(4), pages 1-13, February.
    10. Changchun Peng & Zhijun Xie & Xing Jin, 2024. "Using Ensemble Learning for Remote Sensing Inversion of Water Quality Parameters in Poyang Lake," Sustainability, MDPI, vol. 16(8), pages 1-19, April.
    11. Yi Pan & Qiqi Yuan & Jinsong Ma & Lachun Wang, 2022. "Improved Daily Spatial Precipitation Estimation by Merging Multi-Source Precipitation Data Based on the Geographically Weighted Regression Method: A Case Study of Taihu Lake Basin, China," IJERPH, MDPI, vol. 19(21), pages 1-18, October.
    12. Alik Ismail-Zadeh, 2022. "Natural hazards and climate change are not drivers of disasters," 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. 111(2), pages 2147-2154, March.
    13. Hong Ngoc Nguyen & Hiroatsu Fukuda & Minh Nguyet Nguyen, 2024. "Assessment of the Susceptibility of Urban Flooding Using GIS with an Analytical Hierarchy Process in Hanoi, Vietnam," Sustainability, MDPI, vol. 16(10), pages 1-25, May.
    14. Hameeda Sultan & Jinyan Zhan & Wajid Rashid & Xi Chu & Eve Bohnett, 2022. "Systematic Review of Multi-Dimensional Vulnerabilities in the Himalayas," IJERPH, MDPI, vol. 19(19), pages 1-20, September.
    15. Shiyao Zhu & Haibo Feng & Qiuhu Shao, 2023. "Evaluating Urban Flood Resilience within the Social-Economic-Natural Complex Ecosystem: A Case Study of Cities in the Yangtze River Delta," Land, MDPI, vol. 12(6), pages 1-22, June.
    16. William N. Rom, 2023. "Annals of Education: Teaching Climate Change and Global Public Health," IJERPH, MDPI, vol. 21(1), pages 1-16, December.
    17. Yating Peng & Bo Liu & Mengliang Zhou, 2022. "Sustainable Livelihoods in Rural Areas under the Shock of Climate Change: Evidence from China Labor-Force Dynamic Survey," Sustainability, MDPI, vol. 14(12), pages 1-21, June.
    18. Maruyama Rentschler,Jun Erik & Avner,Paolo & Marconcini,Mattia & Su,Rui & Strano,Emanuele & Bernard,Louise Alice Karine & Riom,Capucine Anne Veronique & Hallegatte,Stephane, 2022. "Rapid Urban Growth in Flood Zones : Global Evidence since 1985," Policy Research Working Paper Series 10014, The World Bank.
    19. Butros M. Dahu & Khuder Alaboud & Avis Anya Nowbuth & Hunter M. Puckett & Grant J. Scott & Lincoln R. Sheets, 2023. "The Role of Remote Sensing and Geospatial Analysis for Understanding COVID-19 Population Severity: A Systematic Review," IJERPH, MDPI, vol. 20(5), pages 1-15, February.
    20. Baosheng Li & Lei Zhou & Jianhuang Qin & Tianjun Zhou & Dake Chen & Shugui Hou & Raghu Murtugudde, 2023. "Middle east warming in spring enhances summer rainfall over Pakistan," Nature Communications, Nature, vol. 14(1), pages 1-7, December.

    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:gam:jsusta:v:15:y:2023:i:20:p:14928-:d:1260818. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.