Fuzzy Pattern Recognition To Water Trophic Grade In Chao Lake Base On Coordination And Fuzzy Weight

Lake eutrophication is one of global environment problems that human beings are facing with. In recent years,the bad eutrophic status of lake has directly threaten on the fragile ecosystem and the life style of people around the lake.Frequent monitoring and in-tegrated management for lakes are necessary.This study aimed at developing and applying an operativemethod to estimate key water quality parameters related to assess the eutrophic status of lake, such as total N(TN),total P(TP),chlorophyll-a(chl-a),Chemical oxygen demand(COD)and Seechi depth(SD)using multi-spectral remote sensing data.There are big differences among the boundary data of the same indicator in the control standards of lake eutrophication. Values of boundary data can increase nearly exponentially, and values of boundary data increased nearly in geometric progression. In addition, uncertainty emerged in weighted selection and meaning of indicators. Based on the above consideration, a model was established for evaluation of lake quality based on coordination and fuzzy weight among indicators. Logarithmic transformations are taken on both the boundary data of the indicators and the monitoring data of the lake to be evaluated, which imp roves the coordination among the indicators and decreases the non21inearity of the evaluation system. On the basis of defining relative distance measures between the eutrophication standard's boundary values and the monitored data of the lake.The subordinate function was established through it. Finally the ranks of surface water quality were evaluated in according to the maximum subordination. The model was applied to identify trophic state of the western half of Chao Lake. The result was consistent with ecological assessment of result basically.