Water Quality Evaluation Model Research Articles

Research on Visualized Information System of Reservoir Ecotope Based on WebGIS

This research take the reservoir ecotope data as an object, facing reservoir management and decision-making. Saves the space geometric object data and the attribute data integration to the SQL Server2000 relational database through SuperMap SDX+, subsequently realizes the data management and maintenance. Uses JFreeChart technology, with which realizes the historical data front end statistical graph demonstration function. Provides a way to water quality evaluation model sequencing using Java programming language, Supermap Is Java controls and Ajax technology, by which make the reservoir ecotope evaluation more easier. At last, the research gives the Liuxi River reservoir as an example to show the main function and the visual effect.

Quality evaluation and its application to surface water ecosystem based on maximum flux principle

Based on the maximum flux principle (MFP), a water quality evaluation model for surface water ecosystem is presented by using self-organization map (SOM) neural network simulation algorithm from the aspect of systematic structural evolution. This evaluation model is applied to the case of surface water ecosystem in Xindu District of Chengdu City in China. The values reflecting the water quality of five cross-sections of the system at different developing stages are obtained, with stable values of 1.438, 2.952, 1.869, 2.443 and 2.479, respectively. The simulation also indicates that the larger the value, the more serious the water pollution. Furthermore, a classification graph is given to reflect the evolution of structural pattern. The combination of MFP and SOM neural network reveals the formation of different structural patterns in the system during the interaction of internal components. It is shown that a dominant pattern is finally reserved, which starts from a variety of combination patterns for a time period. The results agree with those from traditional evaluation methods, which indicates that the proposed model has high accuracy. This model embodies the evolutionary dynamic mechanisms and characteristics of temporal and spatial changes, which helps to guide the prediction of water quality status of surface water ecosystem.

Estimating nutrient loads in the Passaúna reservoir with FLUX

The present paper describes an application of the FLUX program to obtain the nutrient loads for the Passaúna Reservoir in Curitiba´s Metropolitan Region, in the state of Paraná, Brazil. The difficulties and advantages associated with model usage are listed, highlighting the tools available to increase the efficiency of monitoring systems which provide the elements for water quality evaluation models. Estimates of loads in the tributary watersheds are presented, and the associated export coefficients are obtained, indicative of soil use and nutrient-related pollution levels. A comparison of these export coefficients with published values indicates that unaccounted-for non-point sources are likely to exist in the area of study.

FANN-based surface water quality evaluation model and its application in the Shaoguan area

A fuzzy neural network model is proposed to evaluate water quality. The model contains two parts: first, fuzzy mathematics theory is used to standardize the samples; second, the RBF neural network and the BP neural network are used to train the standardized samples. The proposed model was applied to assess the water quality of 16 sections in 9 rivers in the Shaoguan area in 2005. The evaluation result was compared with that of the RBF neural network method and the reported results in the Shaoguan area in 2005. It indicated that the performance of the proposed fuzzy neural network model is practically feasible in the application of water quality assessment and its operation is simple.