Study On Variable Fuzzy Set Theory And Applications In Water Resources System

The water resource is the substance foundation of the life activity and one of the basic natural resources that the mankind depended on for existence and development. As an important strategic economic resource, water resource is always an important factor that influences the development of human society. So, water resource science is of great importance and wide future in the process of understanding nature and transforming the world.In fact, water resource system is a kind of complex and dynamic system, which have large amount of uncertain factor. The factors effecting water resource systems arise from nature, society economy and techniques. Several problems of water resource systems are uncertain and fuzzy. Traditional mathematics can't be used to deal with a lot of fuzzy events. Based on variable fuzzy set theory, this paper concentrates on relative models and approaches for water resource systems. Some achievements have been made on model parameter variations and spreading models.The major contents and research results are as follows:1. Research on parameter variation of the fuzzy assessment model and its application for water environmental qualityA suggestion is provide to determine the position of M and range [c,d]. In the field of evaluating water environmental quality, this suggestion is used to solve practical problems. Four kinds of situations of model parameterαand p are considered verifying the model stability. The assessment result can help eliminate the effect of model parameters difference.2. Research on the method for allocation of water waste loadsIn the field of allocation of water waste loads, because of all kinds of influencing factors, many old methods and theories become unfortunate. Combining the total amount control theory and unit systematic theory of variable fuzzy sets decision-making, multi indexes of social, economic, environmental and technical system are fully considered to allocate the water waste loads. Operability of allocation plan is improved. A new method is provided for allocation of water waste loads.3. Research on a drought degree evaluation method based on the interactive computing model of variable fuzzy classification Based on the variable fuzzy set theory, parameter variation of the interactive computing model of variable fuzzy classification is considered to verify the model stability. The reliability of assessment result can be provided. A new method is provided for the drought degree evaluation too.4. Research on the classificatory of Water Logging Regions in LiaoNing Province based on neural network model of fuzzy optimizationIt is pointed out that waterlog is different from flood. The neural network model of fuzzy sets is proposed in this paper for the classificatory calculation of 35 water logging regions in Liaoning province. It expresses people's knowledge and experience with fuzzy concepts and utilizes the strong learning ability of neural network. The result is objective. A new method is provided to study waterlog.5. The application of variable fuzzy sets decision-making theory and method to replaced water of the Yellow RiverA problem is studied that a part of water diverted from Yellow River to the areas served by the east-route and mid-route of the South-North Water Transfer Project may be reallocated to the upstream and midstream areas of the Yellow River after commissioning of the east-route and mid-route and be-fore commissioning of the west-route of the South-North Water Transfer Project. The method fully takes account of not only quantitative indexes but also qualitative indexes. The complicated decision-making problem of the water reallocation is predigested to the unit system series. The result can be used in practice.6. The expansion of variable fuzzy pattern recognition model and its application to clear recognitionThe range ofαcan be expanded on condition that the recognition is clear. This idea can expand the applicability of variable fuzzy recognition model. The changing trend has been found about relative membership degree. This trend is important for variable fuzzy recognition model, the example can specify this idea.