Evaluation of Water Quality Management Alternatives to Control Dissolved Oxygen and Un-ionized Ammonia for Ravi River in Pakistan

2012 ◽  
Vol 18 (4) ◽  
pp. 451-469 ◽  
Author(s):  
Husnain Haider ◽  
Waris Ali
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Dissolved Oxygen ◽  
Water Quality Management ◽  
Management Alternatives

scholarly journals River Dissolved Oxygen Prediction Based on Random Forest and LSTM

2021 ◽  
Vol 37 (5) ◽  
pp. 901-910
Author(s):  
Juan Huan ◽  
Bo Chen ◽  
Xian Gen Xu ◽  
Hui Li ◽  
Ming Bao Li ◽  
...  
Keyword(s):  
Water Quality ◽  
Random Forest ◽  
Quality Management ◽  
Dissolved Oxygen ◽  
Prediction Model ◽  
Water Quality Management ◽  
Quality Data ◽  
Mean Square ◽  
Water Quality Data ◽  
Better Than

HighlightsRandom Forest (RF) and LSTM were developed for river DO prediction.PH is the most important feature affecting DO prediction.The model base on RF is better than the model not on RF, and the dimensionality of the input data is reduced by RF.RF-LSTM model is outperformed SVR, RF-SVR, BP, RF-BP, LSTM, RNN models in DO prediction.Abstract. In order to improve the prediction accuracy of dissolved oxygen in rivers, a dissolved oxygen prediction model based on Random Forest (RF) and Long Short Term Memory networks (LSTM) is proposed. First, the Random Forest performs feature selection, which reduces the input dimension of the data and eliminates the influence of irrelevant variables on the prediction of dissolved oxygen. Then build the LSTM river dissolved oxygen prediction model to fit the relationship between water quality data and dissolved oxygen, and finally use real water quality data in the river for verification. The experimental results show that the mean square error (MSE), absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE), and coefficient of determination (R2) of the RF-LSTM model are 0.658, 0.528, 13.502, 0.811, 0.744, respectively, which are better than other models. The RF-LSTM model has good predictive performance and can provide a reference for river water quality management. Keywords: Dissolved oxygen prediction, LSTM, Random forest, Time series, Water quality management.

IMPACT OF ZEBRA MUSSELS ON DISSOLVED OXYGEN RESOURCES: IMPLICATIONS FOR ONONDAGA LAKE AND SENECA RIVER, NY WATER QUALITY MANAGEMENT

2005 ◽  
Vol 2005 (8) ◽  
pp. 7018-7029
Author(s):  
Kevin T. Russell ◽  
James R. Rhea ◽  
David Glaser ◽  
Daniel R. Opdyke ◽  
Joseph J. Mastriano
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Dissolved Oxygen ◽  
Water Quality Management ◽  
Zebra Mussels ◽  
Onondaga Lake

Equity measures for exploring water quality management alternatives

1976 ◽  
Vol 12 (5) ◽  
pp. 845-851 ◽  
Author(s):  
E. Downey Brill ◽  
Jon C. Liebman ◽  
Charles S. ReVelle
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Water Quality Management ◽  
Equity Measures ◽  
Management Alternatives

The Effects of the Yorkshire Rivers on Dissolved Oxygen in the Humber Estuary

1984 ◽  
Vol 16 (5-7) ◽  
pp. 127-137 ◽  
Author(s):  
A M C Edwards ◽  
P W Lai
Keyword(s):  
Water Quality ◽  
Quality Management ◽  
Dissolved Oxygen ◽  
Oxygen Saturation ◽  
Computer Simulations ◽  
Water Quality Management ◽  
Management Tool ◽  
Freshwater Flow ◽  
Related Substances ◽  
Humber Estuary

The tidal rivers of the Humber Estuary System experience depletion of dissolved oxygen in dry, summer weather as a result of the pollution received from effluent discharges and the rivers of industrial Yorkshire. A suite of mathematical models has been developed to predict dissolved oxygen saturation and the concentration of related substances. Computer simulations using the models have been carried out to assess the effects of freshwater flow and oxidisable pollutants on dissolved oxygen levels in the estuary system. The models provide a practicable, water quality management tool for the Humber Estuary.

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