Optimization Design of Monitoring Sections of Qinghe River Basin

K-means fuzzy cluster analysis and matter element analysis have been used to select optimize points of water quality monitoring in Qinghe River, taking 8 monitoring section data in June 2014. K-means fuzzy clustering analysis method is suitable for the processing of continuous data set,and matter element analysis method is for handling multi-objective data set.The two methods are combined to optimize single mathematical statistical method. As a result of optimization , 8 monitoring points can be reduced to 6 monitoring sites ,according to sectional properties and actual situation. Water quality monitoring sections are set up to meet the needs at a minimum cost and maximum efficiency to make the monitoring section (station) have the best overall function.The arrangement of environmental monitoring point has relation to the success of the work of environmental monitoring,and optimal layout is an important link in the manifestation of scientific environmental monitoring(1). Presently,being applied for section optimization across the world,this method is mainly utilized to analyze whether the adjacent section needs to be deleted or not,according to similarity of the monitoring data.The common monitoring section optimization methods are Experience Formula Method (2), Fuzzy Clustering Analysis(3), Multi-Objective Decision Analysis Method(4) , Principal Component Analysis(5), Matter-element Analysis Method(6), Genetic Algorithm(7), Dynamic Degree Method (8) etc. This study is based on fuzzy clustering analysis method ,with the k-means clustering analysis and the matter-element analysis being carried out on monitoring section optimization. K - means clustering method is suitable for processing continuous data and large data set.The matter element analysis method is an effective method for pollutant indexes to handle multiple incompatible problems.