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Research on Drought Monitoring Based on Deep Learning: A Case Study of the Huang-Huai-Hai Region in China

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  • Junwei Zhou

    (College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China)

  • Yanguo Fan

    (College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China)

  • Qingchun Guan

    (College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China)

  • Guangyue Feng

    (College of Oceanography and Space Informatics, China University of Petroleum (East China), Qingdao 266580, China)

Abstract

As climate change intensifies, drought has become a major global engineering and environmental challenge. In critical areas such as agricultural production, accurate drought monitoring is vital for the sustainable development of regional agriculture. Currently, despite extensive use of traditional meteorological stations and remote sensing methods, these approaches have proven to be inadequate in capturing the full extent of drought information and adequately reflecting spatial characteristics. Therefore, to improve the accuracy of drought forecasts and achieve predictions across extensive areas, this paper employs deep learning models, specifically introducing an attention-weighted long short-term memory network model (AW-LSTM), constructs a composite drought monitoring index (CDMI) and validates the model. Results show that: (1) The AW-LSTM model significantly outperforms traditional long short-term memory (LSTM), support vector machine (SVM) and artificial neural network (ANN) models in drought monitoring, offering not only better applicability in meteorological and agricultural drought monitoring but also the ability to accurately predict drought events one month in advance compared to machine learning models, providing a new method for precise and comprehensive regional drought assessment. (2) The Huang-Huai-Hai Plain has shown significant regional variations in drought conditions across different years and months, with the drought situation gradually worsening in the northern part of Hebei Province, Beijing, Tianjin, the southern part of Huai North and the central part of Henan Province from 2001 to 2022, while drought conditions in the northern part of Huai North, southern Shandong Province, western Henan Province and southwestern Hebei Province have been alleviated. (3) During the sowing (June) and harvesting (September) periods for summer maize, the likelihood of drought occurrences is higher, necessitating flexible adjustments to agricultural production strategies to adapt to varying drought conditions.

Suggested Citation

  • Junwei Zhou & Yanguo Fan & Qingchun Guan & Guangyue Feng, 2024. "Research on Drought Monitoring Based on Deep Learning: A Case Study of the Huang-Huai-Hai Region in China," Land, MDPI, vol. 13(5), pages 1-20, May.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:5:p:615-:d:1387913
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    References listed on IDEAS

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    1. Christian Janiesch & Patrick Zschech & Kai Heinrich, 2021. "Machine learning and deep learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 685-695, September.
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