scholarly journals Use of Integrated Observations to Improve 0-36 h Flood Forecasting: Development and Application of a Coupled Atmosphere-Hydrology System in the Nanpan River Basin, China

2012 ◽  
Vol 90C (0) ◽  
pp. 131-144 ◽  
Author(s):  
Lei WANG ◽  
Toshio KOIKE ◽  
Man WANG ◽  
Jianyu LIU ◽  
Jihua SUN ◽  
...  
2016 ◽  
Vol 541 ◽  
pp. 457-470 ◽  
Author(s):  
Eram Artinyan ◽  
Beatrice Vincendon ◽  
Kamelia Kroumova ◽  
Nikolai Nedkov ◽  
Petko Tsarev ◽  
...  

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1387 ◽  
Author(s):  
Le ◽  
Ho ◽  
Lee ◽  
Jung

Flood forecasting is an essential requirement in integrated water resource management. This paper suggests a Long Short-Term Memory (LSTM) neural network model for flood forecasting, where the daily discharge and rainfall were used as input data. Moreover, characteristics of the data sets which may influence the model performance were also of interest. As a result, the Da River basin in Vietnam was chosen and two different combinations of input data sets from before 1985 (when the Hoa Binh dam was built) were used for one-day, two-day, and three-day flowrate forecasting ahead at Hoa Binh Station. The predictive ability of the model is quite impressive: The Nash–Sutcliffe efficiency (NSE) reached 99%, 95%, and 87% corresponding to three forecasting cases, respectively. The findings of this study suggest a viable option for flood forecasting on the Da River in Vietnam, where the river basin stretches between many countries and downstream flows (Vietnam) may fluctuate suddenly due to flood discharge from upstream hydroelectric reservoirs.


Water SA ◽  
2004 ◽  
Vol 29 (3) ◽  
Author(s):  
Huynh Ngoc Phien ◽  
Nguyen Duc Anh Kha

2016 ◽  
Vol 87 ◽  
pp. 01016 ◽  
Author(s):  
Wan Hazdy Azad ◽  
Lariyah Mohd Sidek ◽  
Hidayah Basri ◽  
Chow Ming Fai ◽  
Suhani Saidin ◽  
...  

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