Recreational activities in rivers are gaining popularities in Korea due to increase of household income and rehabilitation of riverside. As a lot of people enjoy recreation in water, proper information on the water quality of rivers should be provided to the public in order to secure the safety of publics. In this research, a River Recreation Index (RRI) model was developed based on fuzzy set theory. RRI is the integrated index of important water quality parameters related to recreational activities in rivers and expressed as point from 0 to 100. The fuzzy synthetic evaluation approach was used to reflect the fuzziness of water quality criteria and uncertainty in water quality data. The procedure of the fuzzy synthetic evaluation is divided into four steps: choosing water quality parameters which are integrated into RRI, classifying the range of each water quality parameter, designing appropriate membership function of each parameter, and summarizing all membership value into the RRI. In this study, DO, pH, chlorophyll a and turbidity were chosen as the parameters and the criteria of these four parameters were determined referring domestic and overseas water quality criteria. Membership function in the model was determined as half-triangular shape because it expressed the fuzziness of water quality criteria well. The values of four water quality parameters were converted to membership value by the half-triangular membership function. Then, RRI was calculated by weighted average of the membership values. The proposed model was applied to Sangdong monitoring station in the Nakdong River, Korea. The application result was compared with both the calculation results based on the crisp water quality criteria and real time water quality index (RTWQI) posted by the Ministry of Environment. The simulation results show that RRI with the fuzzy function showed more reasonable changes corresponding to the trend of the water quality parameters.
Seo, Il Won and Choi, Soo-Yeon, "Development Of River Recreation Index Model Reflecting Fuzziness In Water Quality Data" (2014). CUNY Academic Works.