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Related Experiment Videos

Research on comprehensive drought index prediction model based on CNN-LSTM.

Sinan Wang1,2, Xigang Xing3, Xinyi Zou4

  • 1Institute of Pastoral hydraulic research ，MWR, Hohhot, 010020, China.

Scientific Reports
|June 13, 2026
PubMed
Summary

Related Concept Videos

Responses to Drought and Flooding02:41

Responses to Drought and Flooding

Water plays a significant role in the life cycle of plants. However, insufficient or excess of water can be detrimental and pose a serious threat to plants.

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This summary is machine-generated.

This study developed a Regional Comprehensive Drought Index (RSDI) using a random forest model. A hybrid CNN-LSTM model significantly improved drought prediction accuracy in the Ordos region, aiding future drought monitoring.

Area of Science:

  • Environmental Science
  • Climate Science
  • Data Science

Background:

  • Ordos, a key region in Inner Mongolia, faces frequent drought disasters hindering economic growth.
  • Droughts are a critical constraint on regional development due to geographical and climatic factors.

Purpose of the Study:

  • To construct a Regional Comprehensive Drought Index (RSDI) for Ordos from 2001-2020.
  • To evaluate the predictive performance of deep learning models (CNN, LSTM, CNN-LSTM) for RSDI.
  • To enhance drought monitoring and prediction in the Ordos region.

Main Methods:

  • Constructed the Regional Comprehensive Drought Index (RSDI) using a random forest model.
  • Utilized temperature, precipitation, NDVI, soil moisture, land use, and DEM as input variables.
Keywords:
CNNDroughtDrought predictionLSTMRegional comprehensive drought index

Related Experiment Videos

  • Employed Convolutional Neural Network (CNN), Long Short-Term Memory Network (LSTM), and a hybrid CNN-LSTM model for RSDI prediction.

    • Main Results:

    • The constructed RSDI showed a highly significant positive correlation with SPI (r > 0.90, P < 0.01).
    • The hybrid CNN-LSTM model demonstrated superior predictive and fitting performance compared to individual CNN and LSTM models.
    • The CNN-LSTM model achieved reduced prediction errors, with RMSE decreasing by 0.30 and 0.22, and MAE by 0.24 and 0.13.

    Conclusions:

    • The hybrid CNN-LSTM model offers improved accuracy for regional drought index prediction.
    • The study provides a valuable technical reference for drought monitoring and prediction in Ordos.
    • Enhanced drought prediction capabilities are crucial for mitigating the impact of drought catastrophes in the region.