IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v105y2021i2d10.1007_s11069-020-04394-x.html
   My bibliography  Save this article

Drought prediction using in situ and remote sensing products with SVM over the Xiang River Basin, China

Author

Listed:
  • Qian Zhu

    (Southeast University)

  • Yulin Luo

    (Southeast University)

  • Dongyang Zhou

    (Southeast University)

  • Yue-Ping Xu

    (Zhejiang University)

  • Guoqing Wang

    (Nanjing Hydraulic Research Institute)

  • Ye Tian

    (Nanjing University of Information Science & Technology)

Abstract

Droughts have caused many damages in many countries and might be aggravated around the world. Therefore, it is urgent to predict and monitor drought accurately. Soil moisture and its corresponding drought index (e.g., soil water deficit index, SWDI) are the key variables to define drought. However, in situ soil moisture observations are inaccessible in many areas. This study applies support vector machine (SVM) by using a new set of inputs to investigate the performance of in situ and remote sensing products (CMORPH-CRT, IMERG V05 and TRMM 3B42V7) for soil moisture and SWDI forecast over the Xiang River Basin. This study also assesses whether the addition of remote sensing soil moisture as input can improve the performance of SWDI prediction. The results are as follows: (1) the new set of inputs is suitable for drought prediction based on SVM; (2) using in situ precipitation as input to SVM shows the best performance for soil moisture prediction, which followed by TRMM 3B42V7, IMERG V05 and CMORPH-CRT; (3) in situ precipitation and IMERG V05 as input are more suitable for indirect SWDI prediction, while CMORPH-CRT and TRMM 3B42V7 are more suitable for direct SWDI prediction; (4) the addition of soil moisture with in situ precipitation or CMORPH-CRT both can improve the performance of direct SWDI prediction; (5) the lead time for drought prediction with SVM over the Xiang River Basin is about 2 weeks.

Suggested Citation

  • Qian Zhu & Yulin Luo & Dongyang Zhou & Yue-Ping Xu & Guoqing Wang & Ye Tian, 2021. "Drought prediction using in situ and remote sensing products with SVM over the Xiang River Basin, China," 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. 105(2), pages 2161-2185, January.
  • Handle: RePEc:spr:nathaz:v:105:y:2021:i:2:d:10.1007_s11069-020-04394-x
    DOI: 10.1007/s11069-020-04394-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-020-04394-x
    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/s11069-020-04394-x?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. Feng, Puyu & Wang, Bin & Liu, De Li & Yu, Qiang, 2019. "Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia," Agricultural Systems, Elsevier, vol. 173(C), pages 303-316.
    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. Rong Fu & Luze Xie & Tao Liu & Binbin Zheng & Yibo Zhang & Shuai Hu, 2023. "A Soil Moisture Prediction Model, Based on Depth and Water Balance Equation: A Case Study of the Xilingol League Grassland," IJERPH, MDPI, vol. 20(2), pages 1-18, January.
    2. Yang Liu & Li Hu Wang & Li Bo Yang & Xue Mei Liu, 2022. "Drought prediction based on an improved VMD-OS-QR-ELM model," PLOS ONE, Public Library of Science, vol. 17(1), pages 1-13, January.
    3. Israel R. Orimoloye & Adeyemi O. Olusola & Johanes A. Belle & Chaitanya B. Pande & Olusola O. Ololade, 2022. "Drought disaster monitoring and land use dynamics: identification of drought drivers using regression-based algorithms," 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(2), pages 1085-1106, June.

    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. Natalie Teale & David A. Robinson, 2022. "Long-term variability in atmospheric moisture transport and relationship with heavy precipitation in the eastern USA," Climatic Change, Springer, vol. 175(1), pages 1-23, November.
    2. Md. Monirul Islam & Shusuke Matsushita & Ryozo Noguchi & Tofael Ahamed, 2022. "A damage-based crop insurance system for flash flooding: a satellite remote sensing and econometric approach," Asia-Pacific Journal of Regional Science, Springer, vol. 6(1), pages 47-89, February.
    3. Israel R. Orimoloye & Adeyemi O. Olusola & Johanes A. Belle & Chaitanya B. Pande & Olusola O. Ololade, 2022. "Drought disaster monitoring and land use dynamics: identification of drought drivers using regression-based algorithms," 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(2), pages 1085-1106, June.
    4. Farshad Ahmadi & Saeid Mehdizadeh & Babak Mohammadi, 2021. "Development of Bio-Inspired- and Wavelet-Based Hybrid Models for Reconnaissance Drought Index Modeling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 35(12), pages 4127-4147, September.
    5. Lei Liu & Jianqin Ma & Xiuping Hao & Qingyun Li, 2019. "Limitations of Water Resources to Crop Water Requirement in the Irrigation Districts along the Lower Reach of the Yellow River in China," Sustainability, MDPI, vol. 11(17), pages 1-18, August.
    6. Xiao, Xin & Ming, Wenting & Luo, Xuan & Yang, Luyi & Li, Meng & Yang, Pengwu & Ji, Xuan & Li, Yungang, 2024. "Leveraging multisource data for accurate agricultural drought monitoring: A hybrid deep learning model," Agricultural Water Management, Elsevier, vol. 293(C).
    7. Ji Eun Kim & Jisoo Yu & Jae-Hee Ryu & Joo-Heon Lee & Tae-Woong Kim, 2021. "Assessment of regional drought vulnerability and risk using principal component analysis and a Gaussian mixture model," 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. 109(1), pages 707-724, October.
    8. Endre Harsányi & Bashar Bashir & Firas Alsilibe & Muhammad Farhan Ul Moazzam & Tamás Ratonyi & Abdullah Alsalman & Adrienn Széles & Aniko Nyeki & István Takács & Safwan Mohammed, 2022. "Predicting Modified Fournier Index by Using Artificial Neural Network in Central Europe," IJERPH, MDPI, vol. 19(17), pages 1-19, August.
    9. Ruchika Nanwani & Md Mahmudul Hasan & Silvia Cirstea, 2023. "Techniques used to predict climate risks: a brief literature survey," 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(2), pages 925-951, September.
    10. Ning Luo & Qingfeng Meng & Puyu Feng & Ziren Qu & Yonghong Yu & De Li Liu & Christoph Müller & Pu Wang, 2023. "China can be self-sufficient in maize production by 2030 with optimal crop management," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    11. Xu, Zhenheng & Sun, Hao & Zhang, Tian & Xu, Huanyu & Wu, Dan & Gao, JinHua, 2023. "Evaluating established deep learning methods in constructing integrated remote sensing drought index: A case study in China," Agricultural Water Management, Elsevier, vol. 286(C).
    12. Jia, Zhicheng & Ou, Chengming & Sun, Shoujiang & Sun, Ming & Zhao, Yihong & Li, Changran & Zhao, Shiqiang & Wang, Juan & Jia, Shangang & Mao, Peisheng, 2024. "Optimizing drip irrigation managements to improve alfalfa seed yield in semiarid region," Agricultural Water Management, Elsevier, vol. 297(C).
    13. Bishal Poudel & Dewasis Dahal & Mandip Banjara & Ajay Kalra, 2024. "Assessing Meteorological Drought Patterns and Forecasting Accuracy with SPI and SPEI Using Machine Learning Models," Forecasting, MDPI, vol. 6(4), pages 1-19, November.

    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:105:y:2021:i:2:d:10.1007_s11069-020-04394-x. 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.