Water Quality Prediction of Gangapur Reservoir (India) Using LS-SVM and Genetic Programming

2015 ◽  
Vol 20 (4) ◽  
pp. 275-284 ◽  
Author(s):  
Mrunalini Shivaji Jadhav ◽  
Kanchan Chandrashekhar Khare ◽  
Arundhati Suresh Warke
Keyword(s):  
Water Quality ◽  
Genetic Programming ◽  
Quality Prediction ◽  
Water Quality Prediction

Integrated modelling under uncertainty in watershed-level assessment and management

2007 ◽  
Vol 56 (8) ◽  
pp. 31-39 ◽  
Author(s):  
J.H. Ham ◽  
C.G. Yoon ◽  
K.W. Jung ◽  
J.H. Jang
Keyword(s):  
Water Quality ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Watershed Management ◽  
Management Practices ◽  
Control Measures ◽  
Integrated Modelling ◽  
Quality Prediction ◽  
Water Quality Prediction ◽  
Modelling System

Uncertainty in water quality model predictions is inevitably high due to natural stochasticity, model uncertainty, and parameter uncertainty. An integrated modelling system (modified-BASINS) under uncertainty is described and demonstrated for use in receiving-water quality prediction and watershed management. A Monte Carlo simulation was used to investigate the effect of various uncertainty types on output prediction. Without pollution control measures in the watershed, the concentrations of total nitrogen (T-N) and total phosphorus (T-P) in the Hwaong Reservoir, considering three uncertainty types, would be less than about 4.4 and 0.23 mg L−1, respectively, in 2012, with 90% confidence. The effects of two watershed management practices, wastewater treatment plants (WWTP) and constructed wetlands (WETLAND), were evaluated. The combined scenario (WWTP + WETLAND) was the most effective at improving reservoir water quality, bringing concentrations of T-N and T-P in the Hwaong Reservoir to less than 3.4 and 0.14 mg L−1, 24 and 41% improvements, respectively, with 90% confidence. Overall, the Monte Carlo simulation in the integrated modelling system was practical for estimating uncertainty and reliable in water quality prediction. The approach described here may allow decisions to be made based on the probability and level of risk, and its application is recommended.

Water quality prediction model in open limestone systems

2020 ◽  
pp. 279-282
Author(s):  
Louis McDonald ◽  
Qingyun Sun ◽  
Jeffrey Skousen ◽  
Paul Ziemkiewicz
Keyword(s):  
Water Quality ◽  
Prediction Model ◽  
Quality Prediction ◽  
Water Quality Prediction
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