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Zheng Duan

Biträdande universitetslektor

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Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China

Författare

  • Zixuan Chen
  • Guojie Wang
  • Xikun Wei
  • Yi Liu
  • Zheng Duan
  • Yifan Hu
  • Huiyan Jiang

Summary, in English

Drought is a natural disaster that occurs globally and can damage the environment, disrupt agricultural production and cause large economic losses. The accurate prediction of drought can effectively reduce the impacts of droughts. Deep learning methods have shown promise in drought prediction, with convolutional neural networks (CNNs) being particularly effective in handling spatial information. In this study, we employed a deep learning approach to predict drought in the Fenhe River (FHR) basin, taking into account the meteorological conditions of surrounding regions. We used the daily SAPEI (Standardized Antecedent Precipitation Evapotranspiration Index) as the drought evaluation index. Our results demonstrate the effectiveness of the CNN model in predicting drought events 1~10 days in advance. We evaluated the predictions made by the model; the average Nash–Sutcliffe efficiency (NSE) between the predicted and true values for the next 10 days was 0.71. While the prediction accuracy slightly decreased with longer prediction lengths, the model remained stable and effective in predicting heavy drought events that are typically difficult to predict. Additionally, key meteorological variables for drought predictions were identified, and we found that training the CNN model with these key variables led to higher prediction accuracy than training it with all variables. This study approves an effective deep learning approach for daily drought prediction, particularly when considering the meteorological conditions of surrounding regions.

Avdelning/ar

  • Institutionen för naturgeografi och ekosystemvetenskap
  • BECC: Biodiversity and Ecosystem services in a Changing Climate
  • MERGE: ModElling the Regional and Global Earth system

Publiceringsår

2024

Språk

Engelska

Publikation/Tidskrift/Serie

Atmosphere

Volym

15

Issue

2

Dokumenttyp

Artikel i tidskrift

Förlag

MDPI AG

Ämne

  • Oceanography, Hydrology, Water Resources

Nyckelord

  • CNN
  • deep learning
  • drought
  • prediction

Status

Published

ISBN/ISSN/Övrigt

  • ISSN: 2073-4433