Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China
(2024) In Atmosphere 15(2).- Abstract
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... (More)
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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- author
- Chen, Zixuan ; Wang, Guojie ; Wei, Xikun ; Liu, Yi ; Duan, Zheng LU ; Hu, Yifan and Jiang, Huiyan
- organization
- publishing date
- 2024
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- CNN, deep learning, drought, prediction
- in
- Atmosphere
- volume
- 15
- issue
- 2
- article number
- 155
- publisher
- MDPI AG
- external identifiers
-
- scopus:85185928764
- ISSN
- 2073-4433
- DOI
- 10.3390/atmos15020155
- language
- English
- LU publication?
- yes
- id
- 187598ad-8476-4c7e-ab58-d5b4f7d71626
- date added to LUP
- 2024-03-26 15:41:25
- date last changed
- 2024-03-26 15:42:21
@article{187598ad-8476-4c7e-ab58-d5b4f7d71626,
abstract = {{<p>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.</p>}},
author = {{Chen, Zixuan and Wang, Guojie and Wei, Xikun and Liu, Yi and Duan, Zheng and Hu, Yifan and Jiang, Huiyan}},
issn = {{2073-4433}},
keywords = {{CNN; deep learning; drought; prediction}},
language = {{eng}},
number = {{2}},
publisher = {{MDPI AG}},
series = {{Atmosphere}},
title = {{Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China}},
url = {{http://dx.doi.org/10.3390/atmos15020155}},
doi = {{10.3390/atmos15020155}},
volume = {{15}},
year = {{2024}},
}