A large number of parameters are acquired during practical water quality monitoring. If all the parameters are used in water quality assessment, the computational complexity will definitely increase. In order to reduce the input space dimensions, a fuzzy rough set was introduced to perform attribute reduction. Then, an attribute recognition theoretical model and entropy method were combined to assess water quality in the Harbin reach of the Songhuajiang River in China. A dataset consisting of ten parameters was collected from January to October in 2012. Fuzzy rough set was applied to reduce the ten parameters to four parameters: BOD₅, NH₃-N, TP, and F. coli (Reduct A). Considering that DO is a usual parameter in water quality assessment, another reduct, including DO, BOD₅, NH₃-N, TP, TN, F, and F. coli (Reduct B), was obtained. The assessment results of Reduct B show a good consistency with those of Reduct A, and this means that DO is not always necessary to assess water quality. The results with attribute reduction are not exactly the same as those without attribute reduction, which can be attributed to the α value decided by subjective experience. The assessment results gained by the fuzzy rough set obviously reduce computational complexity, and are acceptable and reliable. The model proposed in this paper enhances the water quality assessment system.

Correctly understanding and handling the relationship of tourism industry, ecological environment, and regional economy is an important prerequisite and foundation for realizing regional ecological protection and high-quality development. Based on the entropy method and the coupling coordination model, this paper conducts quantitative research on the coupling coordination relationship and development law of tourism industry–ecological environment–regional economic (TEE) in various cities in Shandong Province. First, a coupling coordination evaluation system of TEE was constructed to evaluate the comprehensive development level of the three systems in each city in Shandong Province from 2010 to 2017; secondly, based on the coupling coordination model, the relationship among the three systems of each city was analyzed using spatial and temporal dimensions; finally, the gray GM (1, 1) model was used to predict the future coupling coordination degree of the three systems in Shandong Province. The research results show that: (1) the development of the economy and tourism industry of cities in Shandong Province is highly correlated, and the overall trend is increasing. The ecological environment mainly changes first, and then rises. (2) From the perspective of time, the changes in the coupling coordination degree of the three systems are mainly to maintain stability and increase fluctuations, and generally develop in the direction of benign coordination. From a spatial perspective, the coupling coordination degree of the three systems shows significant regional integrity and differences, showing a pattern of high in the east and low in the west. (3) In the next few years, the coupling coordination degree of the three systems will roughly continue the characteristics of changes from 2010 to 2017.

In this study, principal component analysis (PCA) and a self-organising map (SOM) were used to analyse a complex dataset obtained from the river water monitoring stations in the Tolo Harbor and Channel Water Control Zone (Hong Kong), covering the period of 2009–2011. PCA was initially applied to identify the principal components (PCs) among the nonlinear and complex surface water quality parameters. SOM followed PCA, and was implemented to analyze the complex relationships and behaviors of the parameters. The results reveal that PCA reduced the multidimensional parameters to four significant PCs which are combinations of the original ones. The positive and inverse relationships of the parameters were shown explicitly by pattern analysis in the component planes. It was found that PCA and SOM are efficient tools to capture and analyze the behavior of multivariable, complex, and nonlinear related surface water quality data.

An integrated approach using the inverse method and Bayesian approach, combined with a lake eutrophication water quality model, was developed for parameter estimation and water environmental capacity (WEC) analysis. The model was used to support load reduction and effective water quality management in the Taihu Lake system in eastern China. Water quality was surveyed yearly from 1987 to 2010. Total nitrogen (TN) and total phosphorus (TP) were selected as water quality model variables. Decay rates of TN and TP were estimated using the proposed approach. WECs of TN and TP in 2011 were determined based on the estimated decay rates. Results showed that the historical loading was beyond the WEC, thus, reduction of nitrogen and phosphorus input is necessary to meet water quality goals. Then WEC and allowable discharge capacity (ADC) in 2015 and 2020 were predicted. The reduction ratios of ADC during these years were also provided. All of these enable decision makers to assess the influence of each loading and visualize potential load reductions under different water quality goals, and then to formulate a reasonable water quality management strategy.