Predicting recycling behaviour: Comparison of a linear regression model and a fuzzy logic model

被引:25
|
作者
Vesely, Stepan [1 ]
Klockner, Christian A. [2 ]
Dohnal, Mirko [1 ]
机构
[1] Brno Univ Technol, Dept Econ, Fac Business & Management, CS-61090 Brno, Czech Republic
[2] Norwegian Univ Sci & Technol, Dept Psychol, Oslo, Norway
关键词
Recycling behaviour; Fuzzy logic; Prediction; Empirical test; SOLID-WASTE MANAGEMENT; PROGRAMMING APPROACH; DECISION-MAKING; SYSTEM; ALTERNATIVES; PERFORMANCE;
D O I
10.1016/j.wasman.2015.12.025
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
In this paper we demonstrate that fuzzy logic can provide a better tool for predicting recycling behaviour than the customarily used linear regression. To show this, we take a set of empirical data on recycling behaviour (N = 664), which we randomly divide into two halves. The first half is used to estimate a linear regression model of recycling behaviour, and to develop a fuzzy logic model of recycling behaviour. As the first comparison, the fit of both models to the data included in estimation of the models (N = 332) is evaluated. As the second comparison, predictive accuracy of both models for "new" cases (hold -out data not included in building the models, N = 332) is assessed. In both cases, the fuzzy logic model significantly outperforms the regression model in terms of fit. To conclude, when accurate predictions of recycling and possibly other environmental behaviours are needed, fuzzy logic modelling seems to be a promising technique. (C) 2015 Elsevier Ltd. All rights reserved.
引用
收藏
页码:530 / 536
页数:7
相关论文
共 50 条
  • [11] Predicting multivariate response in linear regression model
    Srivastava, MS
    Solanky, TKS
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2003, 32 (02) : 389 - 409
  • [12] Estimating the parameters of a fuzzy linear regression model
    Arabpour, A. R.
    Tata, M.
    IRANIAN JOURNAL OF FUZZY SYSTEMS, 2008, 5 (02): : 1 - 19
  • [13] A LINEAR-REGRESSION MODEL WITH FUZZY FUNCTIONS
    TANAKA, H
    UEJIMA, S
    ASAI, K
    JOURNAL OF THE OPERATIONS RESEARCH SOCIETY OF JAPAN, 1982, 25 (02) : 162 - 174
  • [14] Interval estimation for a fuzzy linear regression model
    Yoon, Jin Hee
    Kim, Hae Kyung
    Jung, Hye-Young
    Lee, Woo-Joo
    Choi, Seung Hoe
    PROCEEDINGS OF THE 20TH CZECH-JAPAN SEMINAR ON DATA ANALYSIS AND DECISION MAKING UNDER UNCERTAINTY, 2017, : 227 - 233
  • [15] LINEAR-REGRESSION ANALYSIS WITH FUZZY MODEL
    TANAKA, H
    UEJIMA, S
    ASAI, K
    IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1982, 12 (06): : 903 - 907
  • [16] A Linear Regression Model for Nonlinear Fuzzy Data
    Figueroa-Garcia, Juan C.
    Rodriguez-Lopez, Jesus
    BIO-INSPIRED COMPUTING AND APPLICATIONS, 2012, 6840 : 353 - 360
  • [17] Comparison of the neural network model and linear regression model for predicting the intermingled yarn breaking strength and elongation
    Ozkan, Ilkan
    Kuvvetli, Yusuf
    Baykal, Pinar Duru
    Erol, Rizvan
    JOURNAL OF THE TEXTILE INSTITUTE, 2014, 105 (11) : 1203 - 1211
  • [18] Fuzzy linear regression model based on fuzzy scalar product
    Hsien-Chung Wu
    Soft Computing, 2008, 12 : 469 - 477
  • [19] Fuzzy linear regression model based on fuzzy scalar product
    Wu, Hsien-Chung
    SOFT COMPUTING, 2008, 12 (05) : 469 - 477
  • [20] Detection of epistatic effects with logic regression and a classical linear regression model
    Malina, Magdalena
    Ickstadt, Katja
    Schwender, Holger
    Posch, Martin
    Bogdan, Malgorzata
    STATISTICAL APPLICATIONS IN GENETICS AND MOLECULAR BIOLOGY, 2014, 13 (01) : 83 - 104