Modeling water consumption in public schools in Joinville-SC

Authors

DOI:

https://doi.org/10.46421/entac.v19i1.2151

Keywords:

Water consumption, Schools, Multiple linear regression, Bayesian linear regression

Abstract

This work aims to identify the factors that influence water consumption in schools in the city of Joinville-SC, establishing models for forecasting building water consumption. As the school environment is largely responsible for the transmission of knowledge, including information on water conservation, twenty-six (26) public schools were selected for this research. A survey was carried out on the use of water, through visits and a questionnaire to the management team of each school. The data collected allowed the characterization of schools and a preliminary diagnosis of water use. Water consumption data was obtained from Companhia Águas de Joinville. A descriptive analysis of per capita water consumption was performed, with values ranging from 5.15 to 18.59 liters/student/day. Correlation analysis, multiple linear regression and Bayesian linear regression were used. The results of the correlation analysis reveal that the higher the average income in the neighborhood where the school is located and the area per student ratio, the higher the per capita water consumption. On the other hand, the number of students has a negative correlation with per capita consumption. In the statistical modeling, the independent variable area per student ratio was significant.

Author Biographies

Jéssica Daiane Cunha Schutt, State University pf Santa Catarina

Master's Degree in Civil Engineering from the State University of Santa Catarina - Joinville - SC - Brazil.

Andreza Kalbusch, State University of Santa Catarina

PhD in Civil Engineering from the Federal University of Santa Catarina. Associate Professor at the State University of Santa Catarina - Joinville - SC - Brazil.

Elisa Henning, State University of Santa Catarina

PhD in Production Engineering from the Federal University of Santa Catarina. Associate Professor at the State University of Santa Catarina - Joinville - SC - Brazil.

References

CRUZ, A.O. De La; ALVAREZ-CHAVEZ, C.R.; RAMOS-CORELLA, M.A.; SOTO-HERNANDEZ, F. Determinants of domestic water consumption in Hermosillo, Sonora, Mexico. Jounal of Cleaner Production, v.142, p.1901-1910, 2017. https://doi.org/10.1016/j.jclepro.2016.11.094

EL-NWSANY, R.I.; MAAROUF, I.; ABDEL-AAL, W.A. Water management as a vital factor for a sustainable school. Alexandria Engineering Journal, v.58, n.1, p. 303-313, 2019. https://doi.org/10.1016/j.aej.2018.12.012

FAN, L.; GAI, L.; TONG, Y.; LI, R. Urban water consumption and its influencing factors in China: Evidence from 286 cities. Journal of Cleaner Production, v. 166, p. 124-133, 2017. https://doi.org/10.1016/j.jclepro.2017.08.044

FARINA, M.; MAGLIONICO, M.; POLLASTRI, M.; STOJKOV, I. Water consumptions in public schools. Procedia Engineering, v.21, p. 929-938, 2011. https://doi.org/10.1016/j.proeng.2011.11.2096

GOODRICH, B.; GABRY, J.; ALI, I.; BRILLEMAN, S. rstanarm: Bayesian applied regression modeling via Stan. R package version 2.21.3. 2022. Disponível em: https://mc-stan.org/rstanarm. Acesso em: 30 maio 2022.

HYNDMAN, R.J.; ATHANASOPOULOS, G. Forecasting: Principles and Practice, 2. ed. OTexts, 2018.

JEFFREYS, H. Theory of Probability, 3. ed., Oxford University Press, Oxford, 1961.

KRUSCHKE, J. Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press, 2014.

MAKOWSKI, D.; BEN-SHACHAR, M. S.; LÜDECKE, D. bayestestR: Describing Effects and their Uncertainty, Existence and Significance within the Bayesian Framework. Journal of Open Source Software, v. 4, n. 40, 1541, 2019. https://doi.org/10.21105/joss.01541

MAKOWSKI, D.; BEN-SHACHAR, M. S.; CHEN, S. H. A.; LÜDECKE, D. Indices of Effect Existence and Significance in the Bayesian Framework. Frontiers in Psychology, v. 10, 2767, 2019. doi: 10.3389/fpsyg.2019.02767.

MARINHO, M.; GONÇALVES, M. DO S.; KIPERSTOK, A. Water conservation as a tool to support sustainable practices in a Brazilian public university. Journal of Cleaner Production, v. 62, p. 98-106, 2014. https://doi.org/10.1016/j.jclepro.2013.06.053

MONTGOMERY, D.C.; RUNGER, G.C. Applied Statistics and Probability for Engineers, 6. ed., John Wiley & Sons , Hoboken, 2014.

OLIVER, N.; BRÜMMER, D. Factors influencing water consumption in South Africa schools. Journal of Engineering Design and Technology, v. 5, n.1, p. 81–94, 2007. https://doi.org/10.1108/17260530710746623

PENNY, W.D.; MATTOUT, J.; TRUJILLO-BARRETO, N. Bayesian model selection and averaging. In: Statistical Parametric Mapping, Academic Press, 2007, p. 454-467. https://doi.org/10.1016/B978-012372560-8/50035-8.

RAFTERY, A. E. Bayesian model selection in social research. Sociological methodology, v. 25, p. 111-164, 1995.

R CORE TEAM. R: A language and environment for statistical computing. 2022. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. Acesso em: 30 maio 2022.

SECRETARIA DE ESTADO DA EDUCAÇÃO. Disponível em: <http://serieweb.sed.sc.gov.br/cadueportal.aspx>. Acesso em: 10 set. 2016.

UNESCO. The United Nations World Water Development Report 2015: Water for a Sustainable World. Paris: UNESCO. 139p.

Published

2022-11-07

How to Cite

SCHUTT, Jéssica Daiane Cunha; KALBUSCH, Andreza; HENNING, Elisa. Modeling water consumption in public schools in Joinville-SC . In: NATIONAL MEETING OF BUILT ENVIRONMENT TECHNOLOGY, 19., 2022. Anais [...]. Porto Alegre: ANTAC, 2022. p. 1–11. DOI: 10.46421/entac.v19i1.2151. Disponível em: https://eventos.antac.org.br/index.php/entac/article/view/2151. Acesso em: 22 jul. 2024.

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