scholarly journals Volume Visualization of Water Quality Data in Hamilton Harbour, Lake Ontario

1996 ◽  
pp. 278-286
Author(s):  
Efraim Halfon
Keyword(s):  
Water Quality ◽  
Lake Ontario ◽  
Volume Visualization ◽  
Quality Data ◽  
Water Quality Data ◽  
Hamilton Harbour

Prediction of Chlorophyll and Phosphorus in Lake Ontario by Ensemble of Neural Network Models

2020 ◽  
Author(s):  
Youyue Sun ◽  
Yu Li ◽  
Jinhui Jeanne Huang ◽  
Edward McBean
Keyword(s):  
Neural Network ◽  
Water Quality ◽  
Goodness Of Fit ◽  
Lake Ontario ◽  
Control Measures ◽  
Quality Data ◽  
Group Method ◽  
Generalized Regression Neural Network ◽  
Water Quality Control ◽  
Water Quality Data

<p>Chlorophyll-a (CHLA) and total phosphorous (TP) are key indicators for water quality and eutrophication in lakes. It would be a great help to water management if CHLA and TP could be predicted with certain leading time to ensure water quality control measures could be implemented. Since eutrophication is the results of a complex bio-chemical-physical processes involving in pH, temperature, dissolved oxygen (DO) and many other water quality parameters, the discover of their internal correlations and relationships may help in the predication of CHLA and TP. In this study, a long term (20 years) water quality data including CHLA, TP, total nitrogen (TN), turbidity (TB), sulphate, pH, and DO collected in Lake Ontario by the Environment and Climate Change Canada agency were obtained. These data were analyzed by using a group of Neural Network (NN) models and ensemble strategies were evaluated in this study. One particular ensemble of the following NN models, namely, back propagation, Kohonen, probabilistic neural network (PNN), generalized regression neural network (GRNN), or group method of data handling (GMDH) were selected which has higher goodness of fit and shows robustness in model validation. Comparing with one single NN model, the ensemble model could provide more accurate predictions of CHLA and TP concentration in Lake Ontario and the predication of CHLA and TP would be helpful in lake management, eco-restoration and public health risk assessment.</p>

Editing and Sharing Water Quality Data in Yukon Alaska

2012 ◽  
Author(s):  
Gregg Breton
Keyword(s):  
Water Quality ◽  
Quality Data ◽  
Water Quality Data

Ground-Water Quality Data in the San Francisco Bay Study Unit, 2007: Results from the California GAMA Program

Data Series ◽  
10.3133/ds396 ◽  
2009 ◽  
Author(s):  
Mary C. Ray ◽  
Justin T. Kulongoski ◽  
Kenneth Belitz
Keyword(s):  
Water Quality ◽  
Ground Water ◽  
San Francisco ◽  
San Francisco Bay ◽  
Quality Data ◽  
Ground Water Quality ◽  
Water Quality Data ◽  
Study Unit

Ground-Water Quality Data in the Coastal Los Angeles Basin Study Unit, 2006: Results from the California GAMA Program

Data Series ◽  
10.3133/ds387 ◽  
2008 ◽  
Author(s):  
Timothy M. Mathany ◽  
Michael Land ◽  
Kenneth Belitz
Keyword(s):  
Water Quality ◽  
Los Angeles ◽  
Ground Water ◽  
Quality Data ◽  
Ground Water Quality ◽  
Los Angeles Basin ◽  
Water Quality Data ◽  
Study Unit

Water-quality data collected at Lake Anne, Reston, Virginia, 1997-1999

2000 ◽  
Author(s):  
Kathryn M. Conko ◽  
Margaret M. Kennedy ◽  
Karen C. Rice
Keyword(s):  
Water Quality ◽  
Quality Data ◽  
Water Quality Data

Water-quality data from two agricultural drainage basins in northwestern Indiana and northeastern Illinois: I. Lagrangian and synoptic data, 1999-2002

2005 ◽  
Author(s):  
Ronald C. Antweiler ◽  
Richard L. Smith ◽  
Mary A. Voytek ◽  
John Karl Bohlke ◽  
Kevin D. Richards
Keyword(s):  
Water Quality ◽  
Quality Data ◽  
Agricultural Drainage ◽  
Drainage Basins ◽  
Water Quality Data
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