IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v115y2023i2d10.1007_s11069-022-05588-1.html
   My bibliography  Save this article

Using distributed root soil moisture data to enhance the performance of rainfall thresholds for landslide warning

Author

Listed:
  • Yuxin Guo

    (China University of Geosciences
    National Engineering Research Center of Geographic Information System)

  • Zhanya Xu

    (China University of Geosciences
    National Engineering Research Center of Geographic Information System)

  • Shuang Zhu

    (China University of Geosciences
    National Engineering Research Center of Geographic Information System)

  • Xiangang Luo

    (China University of Geosciences
    National Engineering Research Center of Geographic Information System)

  • Yinli Xiao

    (China University of Geosciences
    National Engineering Research Center of Geographic Information System)

Abstract

Rainfall-induced landslides are currently one of the most frequent disasters in China. Compared with rainfall, the increase of soil moisture and its continuous infiltration of soil are the direct factors leading to landslides. However, few researches have studied landslide forecasting taking the soil moisture into consideration. In addition, soil moisture data have an important depth attribute. Soil moisture in root zone is difficult to obtain, and the separate comparison of the impact of the root zone and shallow soil moisture on landslide is more scarce. After comparing the commonly used satellite data, this article chose the CLDAS-V2.0 data set as the source of soil moisture with the depths of 0–10 cm and 100–200 cm. One hundred and sixty-six rainfall-induced landslides that occurred in Tongzi and Xishui counties from February to July 2020 were studied. This paper first obtains the effective rainfall that has the strongest correlation with the landslide and then uses the effective rainfall to explore the best combination of rainfall and soil moisture, including separate modeling and joint modeling of rainfall and soil moisture. Then support vector machine, logistic regression and three decision tree models are developed to predict the landslides. The results show that the combined model of rainfall and soil moisture is better than the model that only considers rainfall or soil moisture, and the landslide forecasting accuracy is improved by more than 5%, which is about 30% higher than the traditional ED rainfall threshold method. Landslide prediction model is proposed to be used as a help for urban planning and government decision-making.

Suggested Citation

  • Yuxin Guo & Zhanya Xu & Shuang Zhu & Xiangang Luo & Yinli Xiao, 2023. "Using distributed root soil moisture data to enhance the performance of rainfall thresholds for landslide warning," 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. 115(2), pages 1167-1192, January.
    • Handle: RePEc:spr:nathaz:v:115:y:2023:i:2:d:10.1007_s11069-022-05588-1
    DOI: 10.1007/s11069-022-05588-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-022-05588-1
    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/s11069-022-05588-1?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. H. Pourghasemi & H. Moradi & S. Fatemi Aghda, 2013. "Landslide susceptibility mapping by binary logistic regression, analytical hierarchy process, and statistical index models and assessment of their performances," 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. 69(1), pages 749-779, October.
    2. Martin Kuradusenge & Santhi Kumaran & Marco Zennaro, 2020. "Rainfall-Induced Landslide Prediction Using Machine Learning Models: The Case of Ngororero District, Rwanda," IJERPH, MDPI, vol. 17(11), pages 1-20, June.
    3. Markus Reichstein & Gustau Camps-Valls & Bjorn Stevens & Martin Jung & Joachim Denzler & Nuno Carvalhais & Prabhat, 2019. "Deep learning and process understanding for data-driven Earth system science," Nature, Nature, vol. 566(7743), pages 195-204, February.
    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. Licheng Liu & Wang Zhou & Kaiyu Guan & Bin Peng & Shaoming Xu & Jinyun Tang & Qing Zhu & Jessica Till & Xiaowei Jia & Chongya Jiang & Sheng Wang & Ziqi Qin & Hui Kong & Robert Grant & Symon Mezbahuddi, 2024. "Knowledge-guided machine learning can improve carbon cycle quantification in agroecosystems," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. Rajesh Khatakho & Dipendra Gautam & Komal Raj Aryal & Vishnu Prasad Pandey & Rajesh Rupakhety & Suraj Lamichhane & Yi-Chung Liu & Khameis Abdouli & Rocky Talchabhadel & Bhesh Raj Thapa & Rabindra Adhi, 2021. "Multi-Hazard Risk Assessment of Kathmandu Valley, Nepal," Sustainability, MDPI, vol. 13(10), pages 1-27, May.
    3. Zhang, Shuangyi & Li, Xichen, 2021. "Future projections of offshore wind energy resources in China using CMIP6 simulations and a deep learning-based downscaling method," Energy, Elsevier, vol. 217(C).
    4. Florian Reiner & Martin Brandt & Xiaoye Tong & David Skole & Ankit Kariryaa & Philippe Ciais & Andrew Davies & Pierre Hiernaux & Jérôme Chave & Maurice Mugabowindekwe & Christian Igel & Stefan Oehmcke, 2023. "More than one quarter of Africa’s tree cover is found outside areas previously classified as forest," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    5. Garyfallos Arabatzis & Georgios Kolkos & Anastasia Stergiadou & Apostolos Kantartzis & Stergios Tampekis, 2024. "Optimal Allocation of Water Reservoirs for Sustainable Wildfire Prevention Planning via AHP-TOPSIS and Forest Road Network Analysis," Sustainability, MDPI, vol. 16(2), pages 1-27, January.
    6. Amin Salehpour Jam & Jamal Mosaffaie & Faramarz Sarfaraz & Samad Shadfar & Rouhangiz Akhtari, 2021. "GIS-based landslide susceptibility mapping using hybrid MCDM models," 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. 108(1), pages 1025-1046, August.
    7. Wang, Yangjun & Liu, Kefeng & Zhang, Ren & Qian, Longxia & Shan, Yulong, 2021. "Feasibility of the Northeast Passage: The role of vessel speed, route planning, and icebreaking assistance determined by sea-ice conditions for the container shipping market during 2020–2030," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 149(C).
    8. Richards, Daniel Rex & Lavorel, Sandra, 2022. "Integrating social media data and machine learning to analyse scenarios of landscape appreciation," Ecosystem Services, Elsevier, vol. 55(C).
    9. Galaz, Victor & Centeno, Miguel A. & Callahan, Peter W. & Causevic, Amar & Patterson, Thayer & Brass, Irina & Baum, Seth & Farber, Darryl & Fischer, Joern & Garcia, David & McPhearson, Timon & Jimenez, 2021. "Artificial intelligence, systemic risks, and sustainability," Technology in Society, Elsevier, vol. 67(C).
    10. Guoxiong Chen & Qiuming Cheng & Timothy W. Lyons & Jun Shen & Frits Agterberg & Ning Huang & Molei Zhao, 2022. "Reconstructing Earth’s atmospheric oxygenation history using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    11. Zhang, Xinru & Hou, Lei & Liu, Jiaquan & Yang, Kai & Chai, Chong & Li, Yanhao & He, Sichen, 2022. "Energy consumption prediction for crude oil pipelines based on integrating mechanism analysis and data mining," Energy, Elsevier, vol. 254(PB).
    12. Zhou, Huanyu & Qiu, Yingning & Feng, Yanhui & Liu, Jing, 2022. "Power prediction of wind turbine in the wake using hybrid physical process and machine learning models," Renewable Energy, Elsevier, vol. 198(C), pages 568-586.
    13. Rana Muhammad Adnan Ikram & Imran Khan & Hossein Moayedi & Atefeh Ahmadi Dehrashid & Ismail Elkhrachy & Binh Nguyen Le, 2024. "Novel evolutionary-optimized neural network for predicting landslide susceptibility," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(7), pages 17687-17719, July.
    14. Deborah Simon Mwakapesa & Yimin Mao & Xiaoji Lan & Yaser Ahangari Nanehkaran, 2023. "Landslide Susceptibility Mapping Using DIvisive ANAlysis (DIANA) and RObust Clustering Using linKs (ROCK) Algorithms, and Comparison of Their Performance," Sustainability, MDPI, vol. 15(5), pages 1-20, February.
    15. Akash Koppa & Dominik Rains & Petra Hulsman & Rafael Poyatos & Diego G. Miralles, 2022. "A deep learning-based hybrid model of global terrestrial evaporation," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    16. Guanyin Shuai & Yan Zhou & Jingli Shao & Yali Cui & Qiulan Zhang & Chaowei Jin & Shuyuan Xu, 2024. "Comparison of Multiple Machine Learning Methods for Correcting Groundwater Levels Predicted by Physics-Based Models," Sustainability, MDPI, vol. 16(2), pages 1-18, January.
    17. Shasha Song & Isaac R. Santos & Huaming Yu & Faming Wang & William C. Burnett & Thomas S. Bianchi & Junyu Dong & Ergang Lian & Bin Zhao & Lawrence Mayer & Qingzhen Yao & Zhigang Yu & Bochao Xu, 2022. "A global assessment of the mixed layer in coastal sediments and implications for carbon storage," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    18. Chao, Xiangrui & Ran, Qin & Chen, Jia & Li, Tie & Qian, Qian & Ergu, Daji, 2022. "Regulatory technology (Reg-Tech) in financial stability supervision: Taxonomy, key methods, applications and future directions," International Review of Financial Analysis, Elsevier, vol. 80(C).
    19. Zhang, Yi & Cheng, Chuntian & Cai, Huaxiang & Jin, Xiaoyu & Jia, Zebin & Wu, Xinyu & Su, Huaying & Yang, Tiantian, 2022. "Long-term stochastic model predictive control and efficiency assessment for hydro-wind-solar renewable energy supply system," Applied Energy, Elsevier, vol. 316(C).
    20. Gürtürk, Mert & Ucar, Ferhat & Erdem, Murat, 2022. "A novel approach to investigate the effects of global warming and exchange rate on the solar power plants," Energy, Elsevier, vol. 239(PD).

    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:115:y:2023:i:2:d:10.1007_s11069-022-05588-1. 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.