POST-BETZE PIT LAKE WATER QUALITY PREDICTION, NEVADA

2006 ◽  
Vol 2006 (2) ◽  
pp. 1863-1886 ◽  
Author(s):  
William M. Schafer ◽  
Mark Logsdon ◽  
Guosheng Zhan ◽  
Ron Espell
Keyword(s):  
Water Quality ◽  
Lake Water ◽  
Quality Prediction ◽  
Pit Lake ◽  
Lake Water Quality ◽  
Water Quality Prediction

Quantifying Lake Water Quality Evolution: Coupled Geochemistry, Hydrodynamics, and Aquatic Ecology in an Acidic Pit Lake

2017 ◽  
Vol 51 (17) ◽  
pp. 9864-9875 ◽  
Author(s):  
S. Ursula Salmon ◽  
Matthew R. Hipsey ◽  
Geoffrey W. Wake ◽  
Gregory N. Ivey ◽  
Carolyn E. Oldham
Keyword(s):  
Water Quality ◽  
Lake Water ◽  
Aquatic Ecology ◽  
Pit Lake ◽  
Lake Water Quality

Controls on pit lake water quality at sixteen open-pit mines in Nevada

1999 ◽  
Vol 14 (5) ◽  
pp. 669-687 ◽  
Author(s):  
Lisa Shevenell ◽  
Katherine A Connors ◽  
Christopher D Henry
Keyword(s):  
Water Quality ◽  
Lake Water ◽  
Open Pit ◽  
Pit Lake ◽  
Lake Water Quality ◽  
Open Pit Mines

scholarly journals Seasonal forecasting of lake water quality and algal bloom risk using a continuous Gaussian Bayesian network

2022 ◽  
Author(s):  
Leah Jackson-Blake ◽  
François Clayer ◽  
Sigrid Haande ◽  
James Sample ◽  
Jannicke Moe
Keyword(s):  
Water Quality ◽  
Bayesian Network ◽  
Lake Water ◽  
Regression Tree ◽  
Predictive Performance ◽  
Lake Water Quality ◽  
Water Quality Prediction ◽  
Chl A ◽  
First Choice ◽  
Gaussian Bayesian Network

Abstract. Freshwater management is challenging, and advance warning that poor water quality was likely, a season ahead, could allow for preventative measures to be put in place. To this end, we developed a Bayesian network (BN) for seasonal lake water quality prediction. BNs have become popular in recent years, but the vast majority are discrete. Here we developed a Gaussian Bayesian network (GBN), a simple class of continuous BN. The aim was to forecast, in spring, total phosphorus (TP), chlorophyll-a (chl-a), cyanobacteria biovolume and water colour for the coming growing season (May–October) in lake Vansjø in southeast Norway. To develop the model, we first identified controls on inter-annual variability in water quality using correlations, scatterplots, regression tree based feature importance analysis and process knowledge. Key predictors identified were lake conditions the previous summer, a TP control on algal variables, a colour-cyanobacteria relationship, and weaker relationships between precipitation and colour and between wind and chl-a. These variables were then included in the GBN and conditional probability densities were fitted using observations (≤ 39 years). GBN predictions had R2 values of 0.37 (cyanobacteria) to 0.75 (colour) and classification errors of 32 % (TP) to 13 % (cyanobacteria). For all but lake colour, including weather nodes did not improve predictive performance (assessed through cross validation). Overall, we found the GBN approach to be well-suited to seasonal water quality forecasting. It was straightforward to produce probabilistic predictions, including the probability of exceeding management-relevant thresholds. The GBN could be purely parameterised using observed data, despite the small dataset. This wasn’t possible using a discrete BN, highlighting a particular advantage of using GBNs when sample sizes are small. Although low interannual variability and high temporal autocorrelation in the study lake meant the GBN performed similarly to a seasonal naïve forecast, we believe the forecasting approach presented could be useful in areas with higher sensitivity to catchment nutrient delivery and seasonal climate, and for forecasting at shorter time scales (e.g. daily to monthly). Despite the parametric constraints of GBNs, their simplicity, together with the relative accessibility of BN software with GBN handling, means they are a good first choice for BN development, particularly when datasets for model training are small.

scholarly journals Pit Lake Water Quality of Central India

2016 ◽  
Vol 08 (01) ◽  
pp. 28-39 ◽  
Author(s):  
Shobhana Ramteke ◽  
Vintee Verma ◽  
Suryakant Chakradhari ◽  
Pravin Kumar Sahu ◽  
Bharat Lal Sahu ◽  
...  
Keyword(s):  
Water Quality ◽  
Lake Water ◽  
Central India ◽  
Pit Lake ◽  
Lake Water Quality

THE AZNALCÓLLAR PIT LAKE – WATER QUALITY AND OPTIONS OF CONTROL

2006 ◽  
Vol 2006 (2) ◽  
pp. 1927-1935 ◽  
Author(s):  
Martin Schultze ◽  
◽  
Kurt Friese ◽  
Javier Sánchez ◽  
Enrique López
Keyword(s):  
Water Quality ◽  
Lake Water ◽  
Pit Lake ◽  
Lake Water Quality

PIT LAKE WATER QUALITY MODELING CALIBRATION OF THE MAIN PIT, TYRONE MINE, NEW MEXICO, USA

2006 ◽  
Vol 2006 (2) ◽  
pp. 2240-2261
Author(s):  
I.F. Walder ◽  
N. Blandford, ◽  
T. Shelly Jr,
Keyword(s):  
Water Quality ◽  
New Mexico ◽  
Lake Water ◽  
Water Quality Modeling ◽  
Pit Lake ◽  
Lake Water Quality ◽  
Quality Modeling

Improvement of Lake Water Quality by using Moving Rapid Water Treatment System

2018 ◽  
Vol 18 (1) ◽  
pp. 107-115 ◽  
Author(s):  
Kwang-Hee Lee ◽  
◽  
Min-Ho Kim ◽  
Nam-Woo An ◽  
Chul-hwi Park
Keyword(s):  
Water Quality ◽  
Water Treatment ◽  
Lake Water ◽  
Treatment System ◽  
Lake Water Quality ◽  
Water Treatment System

An Assessment of Lake Water Quality Index of Manipu Lake of District Ahmedabad, Gujarat

2012 ◽  
Vol 3 (4) ◽  
pp. 448-450 ◽  
Author(s):  
Nikesh G Kotadiya ◽  
◽  
C. A. Acharya C. A. Acharya
Keyword(s):  
Water Quality ◽  
Lake Water ◽  
Water Quality Index ◽  
Quality Index ◽  
Lake Water Quality
Sign in / Sign up

Export Citation Format

Share Document