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A Coupled Model for Forecasting Spatiotemporal Variability of Regional Drought in the Mu Us Sandy Land Using a Meta-Heuristic Algorithm

Author

Listed:
  • Changfu Tong

    (Institute of Water Resources for Pastoral Area Ministry of Water Resources, Hohhot 010020, China)

  • Hongfei Hou

    (Institute of Water Resources for Pastoral Area Ministry of Water Resources, Hohhot 010020, China)

  • Hexiang Zheng

    (Institute of Water Resources for Pastoral Area Ministry of Water Resources, Hohhot 010020, China)

  • Ying Wang

    (Institute of Water Resources for Pastoral Area Ministry of Water Resources, Hohhot 010020, China
    College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Huhhot 010018, China)

  • Jin Liu

    (Institute of Water Resources for Pastoral Area Ministry of Water Resources, Hohhot 010020, China
    College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Huhhot 010018, China)

Abstract

Vegetation plays a vital role in terrestrial ecosystems, and droughts driven by rising temperatures pose significant threats to vegetation health. This study investigates the evolution of vegetation drought from 2010 to 2024 and introduces a deep-learning-based forecasting model for analyzing regional spatial and temporal variations in drought. Extensive time-series remote-sensing data were utilized, and we integrated the Temperature–Vegetation Dryness Index (TVDI), Drought Severity Index (DSI), Evaporation Stress Index (ESI), and the Temperature–Vegetation–Precipitation Dryness Index (TVPDI) to develop a comprehensive methodology for extracting regional vegetation drought characteristics. To mitigate the effects of regional drought non-stationarity on predictive accuracy, we propose a coupling-enhancement strategy that combines the Whale Optimization Algorithm (WOA) with the Informer model, enabling more precise forecasting of long-term regional drought variations. Unlike conventional deep-learning models, this approach introduces rapid convergence and global search capabilities, utilizing a sparse self-attention mechanism that improves performance while reducing model complexity. The results demonstrate that: (1) compared to the traditional Transformer model, test accuracy is improved by 43%; (2) the WOA–Informer model efficiently handles multi-objective forecasting for extended time series, achieving MAE (Mean Absolute Error) ≤ 0.05, MSE (Mean Squared Error) ≤ 0.001, MSPE (Mean Squared Percentage Error) ≤ 0.01, and MAPE (Mean Absolute Percentage Error) ≤ 5%. This research provides advanced predictive tools and precise model support for long-term vegetation restoration efforts.

Suggested Citation

  • Changfu Tong & Hongfei Hou & Hexiang Zheng & Ying Wang & Jin Liu, 2024. "A Coupled Model for Forecasting Spatiotemporal Variability of Regional Drought in the Mu Us Sandy Land Using a Meta-Heuristic Algorithm," Land, MDPI, vol. 13(11), pages 1-22, October.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:11:p:1731-:d:1504228
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    References listed on IDEAS

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    2. Jamshid Piri & Mohammad Abdolahipour & Behrooz Keshtegar, 2023. "Advanced Machine Learning Model for Prediction of Drought Indices using Hybrid SVR-RSM," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 683-712, January.
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    4. Xinyang Ji & Jinzhong Yang & Jianyu Liu & Xiaomin Du & Wenkai Zhang & Jiafeng Liu & Guangwei Li & Jingkai Guo, 2023. "Analysis of Spatial-Temporal Changes and Driving Forces of Desertification in the Mu Us Sandy Land from 1991 to 2021," Sustainability, MDPI, vol. 15(13), pages 1-17, July.
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