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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
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    References listed on IDEAS

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    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.
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    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.

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