IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v13y2024i5p615-d1387913.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/13/5/615/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/13/5/615/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    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. Jen-Yu Lee & Tien-Thinh Nguyen & Hong-Giang Nguyen & Jen-Yao Lee, 2022. "Towards Predictive Crude Oil Purchase: A Case Study in the USA and Europe," Energies, MDPI, vol. 15(11), pages 1-15, May.
    2. Eduard Hartwich & Alexander Rieger & Johannes Sedlmeir & Dominik Jurek & Gilbert Fridgen, 2023. "Machine economies," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-13, December.
    3. Rainer Alt, 2021. "Electronic Markets on robotics," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 465-471, September.
    4. Najla Alharbi & Bashayer Alkalifah & Ghaida Alqarawi & Murad A. Rassam, 2024. "Countering Social Media Cybercrime Using Deep Learning: Instagram Fake Accounts Detection," Future Internet, MDPI, vol. 16(10), pages 1-22, October.
    5. Abdulwahhab, Ali H. & Abdulaal, Alaa Hussein & Thary Al-Ghrairi, Assad H. & Mohammed, Ali Abdulwahhab & Valizadeh, Morteza, 2024. "Detection of epileptic seizure using EEG signals analysis based on deep learning techniques," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    6. Abhirup Khanna & Bhawna Yadav Lamba & Sapna Jain & Vadim Bolshev & Dmitry Budnikov & Vladimir Panchenko & Alexandr Smirnov, 2023. "Biodiesel Production from Jatropha: A Computational Approach by Means of Artificial Intelligence and Genetic Algorithm," Sustainability, MDPI, vol. 15(12), pages 1-33, June.
    7. Rui Ma & Jia Wang & Wei Zhao & Hongjie Guo & Dongnan Dai & Yuliang Yun & Li Li & Fengqi Hao & Jinqiang Bai & Dexin Ma, 2022. "Identification of Maize Seed Varieties Using MobileNetV2 with Improved Attention Mechanism CBAM," Agriculture, MDPI, vol. 13(1), pages 1-16, December.
    8. Dylan Norbert Gono & Herlina Napitupulu & Firdaniza, 2023. "Silver Price Forecasting Using Extreme Gradient Boosting (XGBoost) Method," Mathematics, MDPI, vol. 11(18), pages 1-15, September.
    9. Cheng Yang & Fuhao Sun & Yujie Zou & Zhipeng Lv & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Haoyang Cui, 2024. "A Survey of Photovoltaic Panel Overlay and Fault Detection Methods," Energies, MDPI, vol. 17(4), pages 1-37, February.
    10. Hong, Jichao & Li, Kerui & Liang, Fengwei & Yang, Haixu & Zhang, Chi & Yang, Qianqian & Wang, Jiegang, 2024. "A novel state of health prediction method for battery system in real-world vehicles based on gated recurrent unit neural networks," Energy, Elsevier, vol. 289(C).
    11. Shuai Sang & Lu Li, 2024. "A Novel Variant of LSTM Stock Prediction Method Incorporating Attention Mechanism," Mathematics, MDPI, vol. 12(7), pages 1-20, March.
    12. Vladimir Franki & Darin Majnarić & Alfredo Višković, 2023. "A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector," Energies, MDPI, vol. 16(3), pages 1-35, January.
    13. Thomas Grisold & Christian Janiesch & Maximilian Röglinger & Moe Thandar Wynn, 2022. "Call for Papers, Issue 5/2024," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(6), pages 841-843, December.
    14. Joshua Holstein & Max Schemmer & Johannes Jakubik & Michael Vössing & Gerhard Satzger, 2023. "Sanitizing data for analysis: Designing systems for data understanding," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-18, December.
    15. Jonas Wanner & Lukas-Valentin Herm & Kai Heinrich & Christian Janiesch, 2022. "The effect of transparency and trust on intelligent system acceptance: Evidence from a user-based study," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(4), pages 2079-2102, December.
    16. Yuanyuan Yang & Md Muhie Menul Haque & Dongling Bai & Wei Tang, 2021. "Fault Diagnosis of Electric Motors Using Deep Learning Algorithms and Its Application: A Review," Energies, MDPI, vol. 14(21), pages 1-26, October.
    17. Patrick Zschech, 2023. "Beyond descriptive taxonomies in data analytics: a systematic evaluation approach for data-driven method pipelines," Information Systems and e-Business Management, Springer, vol. 21(1), pages 193-227, March.
    18. Wendou Yan & Xiuying Wang & Shoubiao Tan, 2022. "YOLO-DFAN: Effective High-Altitude Safety Belt Detection Network," Future Internet, MDPI, vol. 14(12), pages 1-12, November.
    19. Julius Peter Landwehr & Niklas Kühl & Jannis Walk & Mario Gnädig, 2022. "Design Knowledge for Deep-Learning-Enabled Image-Based Decision Support Systems," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(6), pages 707-728, December.
    20. Muhammad Waqas Farooq & Faiza Nawaz & Dr. Raja Irfan Sabir, 2024. "To Gain Sustainable Competitive Advantages (SCA) Using Artificial Intelligence (AI) Over Competitors," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(2), pages 1026-1033.

    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:jlands:v:13:y:2024:i:5:p:615-:d:1387913. 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.