Chen, Zixuan ; Wang, Guojie ; Wei, Xikun ; Liu, Yi ; Duan, Zheng ; Hu, Yifan ; Jiang, Huiyan (2024)
Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China.
In: Atmosphere, 2024, 15 (2)
doi: 10.26083/tuprints-00027215
Artikel, Zweitveröffentlichung, Verlagsversion
 | Es ist eine neuere Version dieses Eintrags verfügbar. |
Kurzbeschreibung (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 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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2024 |
| Autor(en): | Chen, Zixuan ; Wang, Guojie ; Wei, Xikun ; Liu, Yi ; Duan, Zheng ; Hu, Yifan ; Jiang, Huiyan |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China |
| Sprache: | Englisch |
| Publikationsjahr: | 7 Mai 2024 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 25 Januar 2024 |
| Ort der Erstveröffentlichung: | Basel |
| Verlag: | MDPI |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | Atmosphere |
| Jahrgang/Volume einer Zeitschrift: | 15 |
| (Heft-)Nummer: | 2 |
| Kollation: | 14 Seiten |
| DOI: | 10.26083/tuprints-00027215 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/27215 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichung DeepGreen |
| Kurzbeschreibung (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 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. |
| Freie Schlagworte: | drought, prediction, deep learning, CNN |
| ID-Nummer: | Artikel-ID: 155 |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-272155 |
| Zusätzliche Informationen: | This article belongs to the Special Issue Drought Monitoring, Prediction and Impacts |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 500 Naturwissenschaften und Mathematik > 550 Geowissenschaften 600 Technik, Medizin, angewandte Wissenschaften > 624 Ingenieurbau und Umwelttechnik |
| Fachbereich(e)/-gebiet(e): | 13 Fachbereich Bau- und Umweltingenieurwissenschaften 13 Fachbereich Bau- und Umweltingenieurwissenschaften > Institut Wasserbau und Wasserwirtschaft 13 Fachbereich Bau- und Umweltingenieurwissenschaften > Institut Wasserbau und Wasserwirtschaft > Fachgebiet Ingenieurhydrologie und Wasserbewirtschaftung |
| Hinterlegungsdatum: | 07 Mai 2024 09:48 |
| Letzte Änderung: | 08 Mai 2024 06:18 |
| PPN: | |
| Export: | |
| Suche nach Titel in: | TUfind oder in Google |
Verfügbare Versionen dieses Eintrags
- Basin-Scale Daily Drought Prediction Using Convolutional Neural Networks in Fenhe River Basin, China. (deposited 07 Mai 2024 09:48) [Gegenwärtig angezeigt]
 |
Frage zum Eintrag |
Optionen (nur für Redakteure)
 |
Redaktionelle Details anzeigen |
Drucken
Impressum