Risk forecasting of pollution accidents based on an integrated Bayesian Network and water quality model for the South to North Water Transfer Project

2016 ◽  
Vol 96 ◽  
pp. 109-116 ◽  
Author(s):  
Caihong Tang ◽  
Yujun Yi ◽  
Zhifeng Yang ◽  
Jie Sun
Keyword(s):  
Water Quality ◽  
Bayesian Network ◽  
Water Transfer ◽  
Water Quality Model ◽  
The South ◽  
Quality Model ◽  
Risk Forecasting ◽  
North Water

Water quality model parameter identification of an open channel in a long distance water transfer project based on finite difference, difference evolution and Monte Carlo

2013 ◽  
Vol 69 (3) ◽  
pp. 587-594 ◽  
Author(s):  
Dongguo Shao ◽  
Haidong Yang ◽  
Yi Xiao ◽  
Biyu Liu
Keyword(s):  
Water Quality ◽  
Open Channel ◽  
Differential Evolution Algorithm ◽  
Water Transfer ◽  
Water Quality Model ◽  
Model Parameters ◽  
Quality Model ◽  
Long Distance ◽  
Mcmc Simulation ◽  
Evolution Algorithm

A new method is proposed based on the finite difference method (FDM), differential evolution algorithm and Markov Chain Monte Carlo (MCMC) simulation to identify water quality model parameters of an open channel in a long distance water transfer project. Firstly, this parameter identification problem is considered as a Bayesian estimation problem and the forward numerical model is solved by FDM, and the posterior probability density function of the parameters is deduced. Then these parameters are estimated using a sampling method with differential evolution algorithm and MCMC simulation. Finally this proposed method is compared with FDM–MCMC by a twin experiment. The results show that the proposed method can be used to identify water quality model parameters of an open channel in a long distance water transfer project under different scenarios better with fewer iterations, higher reliability and anti-noise capability compared with FDM–MCMC. Therefore, it provides a new idea and method to solve the traceability problem in sudden water pollution accidents.

Prediction of water quality in South to North Water Transfer Project of China based on GA-optimized general regression neural network

2014 ◽  
Vol 15 (1) ◽  
pp. 150-157 ◽  
Author(s):  
Zhuomin Wang ◽  
Dongguo Shao ◽  
Haidong Yang ◽  
Shuang Yang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Water Quality ◽  
Water Transfer ◽  
General Regression Neural Network ◽  
Quality Data ◽  
Water Delivery ◽  
The South ◽  
North Water ◽  
General Regression

The safety of water delivery and water quality in the South to North Water Transfer Project of China is important to northern China. Water quality data, flow data and data on factors that influence water quality were collected from 25 May to 26 August, 2013. These data were used to forecast water quality and calculate the relative error when using a genetic algorithm optimized general regression neural network (GA-GRNN) model as well as conventional general regression neural network (GRNN) and genetic algorithm optimized back propagation (GA-BP) models. The GA-GRNN method requires few network parameters and has good network stability, a high learning speed and strong approximation ability. The overall forecasted result of GA-GRNN is the best of three models, of which the root mean square error (RMSE) of every index is nearly the least among three models. The results reveal that the GA-GRNN model is efficient for water quality prediction under normal conditions and it can be used to ensure the security of water delivery and water quality in the South to North Water Transfer Project.

scholarly journals A One-Dimensional Hydrodynamic and Water Quality Model for a Water Transfer Project with Multihydraulic Structures

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Yujun Yi ◽  
Caihong Tang ◽  
Zhifeng Yang ◽  
Shanghong Zhang ◽  
Cheng Zhang
Keyword(s):  
Water Quality ◽  
Flow Patterns ◽  
Water Transfer ◽  
Water Quality Model ◽  
Hydraulic Structures ◽  
Quality Model ◽  
Complex Flow ◽  
Simulation Accuracy ◽  
One Dimensional ◽  
Volatile Phenol

The long Middle Route of the South to North Water Transfer Project is composed of complex hydraulic structures (aqueduct, tunnel, control gate, diversion, culvert, and diverted siphon), which generate complex flow patterns. It is vital to simulate the flow patterns through hydraulic structures, but it is a challenging work to protect water quality and maintain continuous water transfer. A one-dimensional hydrodynamic and water quality model was built to understand the flow and pollutant movement in this project. Preissmann four-point partial-node implicit scheme was used to solve the governing equations in this study. Water flow and pollutant movement were appropriately simulated and the results indicated that this water quality model was comparable to MIKE 11 and had a good performance and accuracy. Simulation accuracy and model uncertainty were analyzed. Based on the validated water quality model, six pollution scenarios (Q1 = 10 m3/s, Q2 = 30 m3/s, and Q3 = 60 m3/s for volatile phenol (VOP) and contaminant mercury (Hg)) were simulated for the MRP. Emergent pollution accidents were forecasted and changes of water quality were analyzed according to the simulations results, which helped to guarantee continuously transferring water for a large water transfer project.

Developing a Cloud-based Toolbox for Sensitivity Analysis of a Water Quality Model

2021 ◽  
pp. 105068
Author(s):  
Soobin Kim ◽  
Yong Sung Kwon ◽  
JongChel Pyo ◽  
Mayzonee Ligaray ◽  
Joong-Hyuk Min ◽  
...  
Keyword(s):  
Water Quality ◽  
Sensitivity Analysis ◽  
Water Quality Model ◽  
Quality Model

Choosing an appropriate water quality model—a review

2021 ◽  
Vol 193 (1) ◽  
Author(s):  
Cássia Monteiro da Silva Burigato Costa ◽  
Izabel Rodrigues Leite ◽  
Aleska Kaufmann Almeida ◽  
Isabel Kaufmann de Almeida
Keyword(s):  
Water Quality ◽  
Water Quality Model ◽  
Quality Model
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