This paper presents the results of regression models (linear, nonlinear and stochastic regression) and artificial neural network models (ANN) using observed data of daily maximum air and water temperature at Bai Chay station in the coastal areas of Northern Delta, Vietnam. The accuracy of the models was evaluated and compared by R, RMSE, RMSE% and E indicators. The ANN model was highlight results with the RMSE = 1.24; R = 0.98; E = 0.9; RMSE% = 4. The results of the study also show that daily water temperature is affected by daily maximum and average air temperature of previous 1 and 2 days. The main contribution of this study is to identify the appropriate models and time lag factors for water temperature prediction from the air temperature applied to neighboring meteorological stations without water temperature monitoring data. The results of the study could be used as a basis for determining the spatial distribution of water temperature risk to aquaculture in the coastal areas of Northern Delta, Vietnam.
Climate change (CC) increases saltwater intrusion, changes water flow and alters the ecological characteristics that lead to significant impact on the farming activities in delta areas. This study defines inland aqua-ecological zones (AEZ) for CC conditions in the Mekong delta region, Vietnam.The hydraulic model Vietnam River Systems and Plains (VRSAP) was used to create maps of salinity and flood depth for three baseline scenarios (1998, 2000 and 2004)
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