Water Quality Assessment Model - Oxygen Dynamics Model for Low-Flow Streams

1977 ◽  
Author(s):  
Stephen P. Shelton
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Water Quality Assessment ◽  
Assessment Model ◽  
Low Flow ◽  
Dynamics Model ◽  
Oxygen Dynamics

scholarly journals Research on Water Quality Assessment Model Based on Improved Three-Dimensional Nemerow Index Method and Firefly Algorithm

2017 ◽  
Vol 7 (2) ◽  
pp. 47
Author(s):  
Jian Cao ◽  
Zheng-Long Li ◽  
Yuan-Biao Zhang
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Firefly Algorithm ◽  
Human Life ◽  
Three Dimensional ◽  
Water Quality Assessment ◽  
Assessment Model ◽  
Index Method ◽  
Threshold Setting ◽  
Ongoing Concern

Monitoring water quality is a subject of ongoing concern and study since water quality is closely related to human life. Although Nemerow index method is widely used in water quality assessment, the artificial threshold setting may lead to some errors. In this study, we improved the traditional Nemerow index method and built a three-dimensional water quality assessment model combined with the modified firefly algorithm (FA). Then, we applied the improved three-dimensional Nemerow index method to evaluate 100 random water samples. Compared with the traditional method, the improved one proved to be more objective, scientific and practical.

scholarly journals A Multiagent Dynamic Assessment Approach for Water Quality Based on Improved Q-Learning Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Jianjun Ni ◽  
Li Ren ◽  
Minghua Liu ◽  
Daqi Zhu
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Water Resource Management ◽  
Learning Algorithm ◽  
Dynamic Assessment ◽  
Water Quality Assessment ◽  
Assessment Model ◽  
Fuzzy Membership Function ◽  
Q Learning ◽  
Interaction Factor

The dynamic water quality assessment is a challenging and critical issue in water resource management systems. To deal with this complex problem, a dynamic water assessment model based on multiagent technology is proposed, and an improved Q-learning algorithm is used in this paper. In the proposed Q-learning algorithm, a fuzzy membership function and a punishment mechanism are introduced to improve the learning speed of Q-learning algorithm. The dynamic water quality assessment for different regions and the prewarning of water pollution are achieved by using an interaction factor in the proposed approach. The proposed approach can deal with various situations, such as static and dynamic water quality assessment. The experimental results show that the water quality assessment based on the proposed approach is more accurate and efficient than the general methods.

Establishment and Application of Water Quality Assessment Model for Jiaozhou Bay Basin

2012 ◽  
Vol 518-523 ◽  
pp. 1165-1170 ◽  
Author(s):  
Chao Liu ◽  
Hui He ◽  
Xiao Hui Tan ◽  
Ai Li Gao ◽  
Song Xue
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Bp Neural Network ◽  
Jiaozhou Bay ◽  
Water Quality Assessment ◽  
Training Sample ◽  
Assessment Method ◽  
Assessment Model ◽  
Water Quality Status ◽  
Effectiveness Of Training

In this paper, a comprehensive water quality assessment model for the seagoing rivers of the Jiaozhou Bay basin was established based on a BP neural network. In the situation investigation, a list of main assessment indexes was selected, comprising COD, permanganate, DO, ammonia, volatile hydroxybenzene and mineral oil. Then Environmental Quality Standards for Surface Water was used as the training sample and comprehensive assessment was conducted for the rivers. In Comparison with results from the conventional single-factor assessment method, this model not only responded to the comprehensive river water quality status, but also improved the speed and effectiveness of training, saving time and increasing accuracy of the assessment model through a series of design optimizations.

Research on the New Assessment Model of Water Source Quality Based on Maximum Entropy Principle

2011 ◽  
Vol 403-408 ◽  
pp. 2123-2126
Author(s):  
Ren Qiang Lu
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Maximum Entropy ◽  
Structural Changes ◽  
Maximum Entropy Principle ◽  
Water Source ◽  
Water Quality Assessment ◽  
Assessment Model ◽  
Lake System ◽  
Entropy Principle

Water source quality is the crucial factor to determine the safety and reliability of urban water supply, and the effective water quality assessment is the premise for water source conservation. Taking a lake system as an example, firstly, the maximum entropy principle was used to analyze the evolution mechanism of the structural changes of lake system. And the nonlinear coupling between the different subsystems of lake system was also analyzed. Secondly, the Self-Organizing feature map neural network was used to simulate the dynamic evolution process of lake system. Finally, a new assessment model for water source quality was established based on the maximum entropy principle. Through application it found that this model achieved the quantitative evaluation of water source quality effectively, and surmounted the inadequate that the precious water quality assessment methods had to determination the weights of the different water quality indicators subjectively.

Development of a water quality assessment model: a water quality assessment model based on watershed characteristics by non-linear regression

2014 ◽  
Vol 15 (2) ◽  
pp. 236-247 ◽  
Author(s):  
Yongdeok Cho
Keyword(s):  
Water Quality ◽  
Linear Regression ◽  
Quality Assessment ◽  
Watershed Modeling ◽  
Oxygen Demand ◽  
Information Criterion ◽  
Water Quality Assessment ◽  
Assessment Model ◽  
Land Usage ◽  
Non Linear

Here, a water quality assessment model (WQAM) is developed by non-linear regression as an alternative to physical watershed modeling in South Korea. Three cases and 10 scenarios are applied and reviewed to determine the most appropriate WQAM. The three cases are: (1) the area size allocation of sub-watersheds, (2) the watershed imperviousness ratio, and (3) the combination of the area size and imperviousness ratio. The 10 scenarios are: (1) impervious, (2) impervious + pervious, (3) impervious + rainfall, (4) impervious + slope, (5) impervious + rainfall + slope, (6) slope, (7) land usage, (8) land usage + rainfall, (9) land usage + slope, and (10) land usage + rainfall + slope. The best WQAMs are subsequently developed from the generated equations using statistics (R2, Adjusted R2, F-test, the Akaike information criterion and the Shapiro–Wilk test). In addition, the WQAM is verified using the Geum-Sum-Young River watershed. The percentage differences of biochemical oxygen demand (BOD), total nitrogen (T-N), and total phosphorus (T-P) are 31.66%, 8.08%, and 48.94%, respectively. The developed WQAM can be used in place of complex watershed modeling and to aid in the determination of the best restoration locations.

scholarly journals A water quality assessment model for Suya Lake Reservoir

2020 ◽  
Vol 20 (8) ◽  
pp. 3715-3721
Author(s):  
Xiaolei Ji ◽  
Xin Wang ◽  
Guoping Yang
Keyword(s):  
Water Quality ◽  
Quality Assessment ◽  
Evaluation Method ◽  
Cold Water ◽  
Comprehensive Evaluation ◽  
Fuzzy Comprehensive Evaluation ◽  
Water Quality Assessment ◽  
Assessment Model ◽  
Fuzzy Comprehensive Evaluation Method ◽  
Comprehensive Evaluation Method

Abstract Reasonable water quality assessment of reservoirs plays an important part in strengthening water source management. Based on the monitoring data on the water quality in Suya Lake Reservoir provided by water quality environmental monitoring agencies, this paper selected typical water quality indexes such as dissolved oxygen, permanganate (CODMn), ammonia nitrogen (NH3-N), total phosphorus (TP) and total nitrogen (TN) as the water quality assessment factors, and applied the fuzzy comprehensive evaluation method to assess the quality of water in the Suya Lake Reservoir on the basis of single factor assessment. The result shows that the fuzzy comprehensive evaluation method can accurately quantify the fuzzy assessment of water quality. The main pollution factors in Suya Lake Reservoir are TP and TN, and pollution mainly occurs in the waters on the west bank of the reservoir (between the Cold Water River and the Lianjiang River), with the Huangxi River and its inlet being the most seriously polluted. This study provides some reference for the improvement to the water quality assessment model and scientific basis for the prevention and control of water environment pollution in the reservoir area.

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