Evaluating established deep learning methods in constructing integrated remote sensing drought index: A case study in China

Xu, Zhenheng; Sun, Hao; Zhang, Tian; Xu, Hongtao; Wu, Dan; Gao, Jinhua

doi:10.1016/j.agwat.2023.108405

Cited by 8 publications

(2 citation statements)

References 55 publications

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“…The higher prediction accuracy obtained by using fewer key variables for training also reflects that deep learning can overcome the limitations of numerical weather prediction [61,62]. Xu et al [26] have demonstrated that deep learning methods can efficiently process time series, while our study takes spatial factors into account, making the prediction of drought more scientific. The training data for the model contain non-meteorological parameters, which also has some limitations.…”

Section: Discussionmentioning

confidence: 72%

“…Mokhtar et al [24] used random forest (RF), extreme gradient boosting (XGB), the convolutional neural network (CNN), and long short-term memory (LSTM) to analyze drought on the eastern edge of the Qinghai-Tibet Plateau, obtaining favorable results. However, most drought predictions have focused on time series and ignored spatial scale impacts [25,26]. Droughts in a particular region are often influenced not only by local factors but also by climate conditions in distant areas.…”

Section: Introductionmentioning

confidence: 99%

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

Chen,

Wang,

Wei

et al. 2024

Atmosphere

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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.

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Section: Discussionmentioning

confidence: 72%