Fuzzy Linear Regression of Rainfall-Altitude Relationship

Proceedings

Nikiforos Samarinas¹  Christos Vrekos¹  Christos Evangelides¹  Christos Tzimopoulos¹ 

[1] Department of Rural and Surveying Engineering, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;

关键词: fuzzy regression;    trapezoidal parameters;    fuzzy linear programming;    possibilistic models;   

DOI  :  10.3390/proceedings2110636

来源: DOAJ

【 摘 要 】

Classical linear regression has been used to measure the relationship between rainfall data and altitude in different meteorological stations, in order to evaluate a linear relation. The values of rainfall are supposed as dependent variables and the values of elevation of each station as independent variables. It has long been known that a classical statistical relationship exists between annual rainfall and the station elevation which in many cases is linear as the one examined in this article. However classical linear regression makes rigid assumptions about the statistical properties of the model, accepting the error terms as random variables, and the violation of this assumption could affect the validity of the classical linear regression. Fuzzy regression assumes ambiguous and imprecise parameters and data. For this reason it may be more effective than classical regression. In this paper we evaluate the relationship between annual rainfall data and the elevation of each station in Thessaly’s meteorological stations, using fuzzy linear regression with trapezoidal membership functions. In this possibilistic model the dependent measured elevations are crisp, and the independent observed rainfall values as well as the parameters of the model are fuzzy.

【 授权许可】

Unknown