Optimized Selection of Wetland Water Quality Monitoring Points Based on Information Entropy and Fuzzy Similarity

Abstract

Known as the kidney of earth, wetland has significant ecological functions such as freshwater conservation, poison elimination, carbon storage, water quality purification, flood storage and drought control, climate regulation and remaining biodiversity etc. So protecting wetland is protecting ourselves. Water environment quality best reflects the ecological environment condition of wetland. According to multi-index and Spatial and Temporal variation of wetland water pollution, combining optimized selection requirements of wetland water quality monitoring, fuzzy similarity is propose. Through constructing multi-index monitoring data samples Decision-making Matrix, fuzzy similarity matrix between sample data and their mean values is established. According to the index value variation, the index weights are calculated based on information entropy theory. With the index weight and sample fuzzy similarity matrix, comprehensive fuzzy similarity of each monitoring point is calculated. Finally, according to comprehensive fuzzy similarity, each monitoring point is reasonably clustered, then representative points is selected from each category, so distribution optimization could be realized. Practical running proves that this scheme is simple and feasible, and extensionally applied to optimize other environmental monitoring points.