Review of Selected References and Data sets on Ambient Ground- and Surface-Water Quality in the Metedeconk River, Toms River, and Kettle Creek Basins, New Jersey, 1980-2001

2003 ◽  
Keyword(s):  
Water Quality ◽  
New Jersey ◽  
Surface Water ◽  
Surface Water Quality ◽  
Data Sets

scholarly journals Assessing the Performance of Deep Learning Algorithms for Short-Term Surface Water Quality Prediction

2021 ◽  
Vol 13 (19) ◽  
pp. 10690
Author(s):  
Heelak Choi ◽  
Sang-Ik Suh ◽  
Su-Hee Kim ◽  
Eun Jin Han ◽  
Seo Jin Ki
Keyword(s):  
Water Quality ◽  
Deep Learning ◽  
Surface Water ◽  
Learning Algorithms ◽  
Arima Model ◽  
Surface Water Quality ◽  
Percentage Error ◽  
Data Sets ◽  
Learning Models ◽  
Dependent Variables

This study aimed to investigate the applicability of deep learning algorithms to (monthly) surface water quality forecasting. A comparison was made between the performance of an autoregressive integrated moving average (ARIMA) model and four deep learning models. All prediction algorithms, except for the ARIMA model working on a single variable, were tested with univariate inputs consisting of one of two dependent variables as well as multivariate inputs containing both dependent and independent variables. We found that deep learning models (6.31–18.78%, in terms of the mean absolute percentage error) showed better performance than the ARIMA model (27.32–404.54%) in univariate data sets, regardless of dependent variables. However, the accuracy of prediction was not improved for all dependent variables in the presence of other associated water quality variables. In addition, changes in the number of input variables, sliding window size (i.e., input and output time steps), and relevant variables (e.g., meteorological and discharge parameters) resulted in wide variation of the predictive accuracy of deep learning models, reaching as high as 377.97%. Therefore, a refined search identifying the optimal values on such influencing factors is recommended to achieve the best performance of any deep learning model in given multivariate data sets.

AN EVALUATION OF SUB-SURFACE WATER QUALITY AROUND BHARUCH-SURAT INDUSTRIAL REGION, GUJARAT, INDIA

2019 ◽  
Vol 38 (2) ◽  
pp. 200-220
Author(s):  
SOMNATH SAHA ◽  
◽  
SUKANTA KUMAR SAHA ◽  
TATHAGATA GHOSH ◽  
ROLEE KANCHAN ◽  
...  
Keyword(s):  
Water Quality ◽  
Surface Water ◽  
Surface Water Quality ◽  
Industrial Region
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