scholarly journals ESTIMATION OF WATER QUALITY VARIATION IN A TIDAL RIVER BY APPLYING DEEP LEARNING MODELS

2017 ◽  
Vol 73 (4) ◽  
pp. I_1141-I_1146 ◽  
Author(s):  
Yusuke NAKATANI ◽  
Masahiro ISHIZAKI ◽  
Shuzo NISHIDA
Keyword(s):  
Water Quality ◽  
Deep Learning ◽  
Tidal River ◽  
Learning Models ◽  
Water Quality Variation ◽  
Quality Variation

Characteristics of physical water quality variation under heat storage in closed saline water aquaculture ponds in the tropics

2021 ◽  
Author(s):  
Akinori Ozaki ◽  
Panitan Kaewjantawee ◽  
Nguyen Van Thinh ◽  
Masaru Matsumoto ◽  
Masayoshi Harada ◽  
...  
Keyword(s):  
Water Quality ◽  
Heat Storage ◽  
Saline Water ◽  
Aquaculture Ponds ◽  
Water Quality Variation ◽  
The Tropics ◽  
Quality Variation

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.

scholarly journals High-frequency monitoring reveals seasonal and event-scale water quality variation in a temporally frozen river

2018 ◽  
Vol 564 ◽  
pp. 619-639 ◽  
Author(s):  
M. Kämäri ◽  
S. Tattari ◽  
E. Lotsari ◽  
J. Koskiaho ◽  
C.E.M. Lloyd
Keyword(s):  
Water Quality ◽  
High Frequency ◽  
Event Scale ◽  
Water Quality Variation ◽  
Frequency Monitoring ◽  
Quality Variation

Water Quality Variation and Biotic Community Characteristics in Juam Lake (2011)

2013 ◽  
Vol 31 (1) ◽  
pp. 37-44 ◽  
Author(s):  
Hyo-Jeong Song ◽  
◽  
Kyung-Sub Hwang ◽  
Jong-Hwan Park ◽  
Hak-Young Lee ◽  
...  
Keyword(s):  
Water Quality ◽  
Community Characteristics ◽  
Biotic Community ◽  
Water Quality Variation ◽  
Quality Variation
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