Some Research On Desertification For Min Feng County Of He Tian In Xin Jiang | | Posted on:2008-08-03 | Degree:Master | Type:Thesis | | Country:China | Candidate:S C Xiong | Full Text:PDF | | GTID:2121360215482931 | Subject:Computational Mathematics | | Abstract/Summary: | PDF Full Text Request | | We collected many kinds of data for over ten years such as the number of thepopulation ,acreage and so on. Then we analyze the di?usion of desert basing onthese data and we get the weights of the main in?uencing factors on this prob-lem. Firstly, we introduce the background of the problem brie?y and analyze therelationship of the local population and environment initially. Because they in?u-ence and restrict for each other, we choose the population as the object of researchfrom so many in?uencing factors for imitating and forecasting the population ofMin Feng county. In the third chapter, we fix the weights of in?uencing factors ondesertificationThe first chapter want to expound that the problem of desertification is veryserious, it hinders the nation economy seriously and destroys the ecological envi-ronment, and one of the main in?uencing factors is the population growth whichmake the environment and development of human loose the balance.The purpose of Chapter 2 is devoted to post a population model with two non-linear terms which agrees with Min Feng county's environment basing on Logisticmodel according to the feature of Min Feng county and considering the type of theland. The population model is as follow :Using this model to imitate and predict the population of this country, we can getgood result.In Chapter 3 we study the factor's weights to Min Feng county of He Tianin Xin Jiang using fuzzy recognition and clustering theory and get to the factors'index.It is the first time to quantify the in?uencing degree on the research of thedesertification in Xin Jiang . The result show that this method is feasible andvalidly. It's valuable to generalize. | | Keywords/Search Tags: | Logistic model, nonlinear term, population model, imitate, predict, fuzzy recognition, fuzzy clustering, desertification, weights | PDF Full Text Request | Related items |
| |
|