PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2021 | No. 54 | 113--122
Tytuł artykułu

The Potential of data Exploration Methods in Identifying the Relationship Between Short-period (daily) water consumption and meteorological factors

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The purpose of the work was to identify the hidden relationship between water consumption and meteorological factors, using principal component analysis. In addition, clusters of similar days were identified based on relationships identified by k-means. The study was based on data from the city of Toruń (Poland). The analysis was based on daily data from 2014-2017 divided into three groups. Group I included data from the entire period, Group II- from warm halfyears (April-September), and Group III-from cold half-years (January-March and October-December). For Groups I and II the extent of water consumption was explained by two principal components. PC1 includes variables that increase water consumption, and PC2 includes variables that lessen water demand. In Group III, water consumption was not linked to any component. The k-means method was used to identify clusters of similar days. In terms of PC1, the most numerous days were Saturdays, and in terms of PC2 Sundays and holidays. It was determined that further research aimed at explaining the specificity of water consumption on particular days of the week is appropriate(original abstract)
Słowa kluczowe
EN
PL
Rocznik
Numer
Strony
113--122
Opis fizyczny
Twórcy
  • Nicolaus Copernicus University, Poland
  • Nicolaus Copernicus University in Toruń
  • Nicolaus Copernicus University in Toruń
Bibliografia
  • Akuoko-Asibey, A., Nkemdirim, L.C., & Draper, D.L. (1993). The impacts of climatic variables on seasonal water consumption in Calgary, Alberta. Canadian Water Resources Journal, 18(2): 107-116. DOI: https:// doi.org/10.4296/cwrj1802107
  • Allen, R.G. (2000). Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study. Journal of Hydrology, 229(1-2): 27-41. DOI: https://doi.org/10.1016/ S0022-1694(99)00194-8
  • Argüelles, M., Benavides, C., & Fernández, I. (2014). A new approach to the identification of regional clusters: hierarchical clustering on principal components. Applied Economics, 46(21): 2511-2519. DOI: https://doi.org/10.1 080/00036846.2014.904491
  • Balicki, A. (2009). Statystyczna analiza wielowymiarowa i jej zastosowania społeczno-ekonomiczne (Statistical multivariate analysis and its socio-economic applications - in Polish). Gdańsk: Wydawnictwo Uniwerytetu Gdańskiego.
  • Berglund, A.E., Welsh, E.A., & Eschrich, S.A. (2017). Characteristics and validation techniques for PCAbased gene-expression signatures. International Journal of Genomics, 2017: 2354564. DOI: https://doi. org/10.1155/2017/2354564
  • Czillingová, J., Petruška, I., & Tkáč, M. (2012). Financial and economic analysis of steel industry by multivariate analysis. Ekonomický časopis (Journal of Economics), 60(4): 388-405. Available at: https://www.sav.sk/journals/ uploads/0622125104 12 Tkac-RS.pdf (3.12.2021). de Souza, A., Aristone, F., Sabbah, I., da Silva Santos, D.A., de Souza Lima, A.P., & Lima, G. (2015).
  • Climatic Variations and Consumption of Urban Water. Atmospheric and Climate Sciences, 5(3): 292-301. DOI: http://dx.doi.org/10.4236/acs.2015.53022
  • Di Salvo, F., Ruggieri, M., & Plaia, A. (2015). Functional principal component analysis for multivariate multidimensional environmental data. Environmental and Ecological Statistics, 22(4): 739-757. DOI: https:// doi.org/10.1007/s10651-015-0317-8
  • Dong, F., Mitchell, P.D., & Colquhoun, J. (2015). Measuring farm sustainability using data envelope analysis with principal components: The case of Wisconsin cranberry. Journal of Environmental Management, 147: 175-183. DOI: https://doi.org/10.1016/j.jenvman.2014.08.025
  • Fernández, S., Cotos-Yáñez, T., Roca-Pardiñas, J., & Ordóñez, C. (2018). Geographically weighted principal components analysis to assess diffuse pollution sources of soil heavy metal: application to rough mountain areas in Northwest Spain. Geoderma, 311: 120-129. DOI: https://doi.org/10.1016/j.geoderma.2016.10.012
  • Gazley, M.F., Collins, K.S., Roberston, J., Hines, B.R., Fisher, L.A., & McFarlane, A. (2015). Application of principal component analysis and cluster analysis to mineral exploration and mine geology. AusIMM New Zealand Branch Annual Conference, 131-139.
  • Gorączko, M., & Pasela, R. (2015). Causes and effects of water consumption drop by the population of cities in Poland - selected aspects. Bulletin of Geography. Socioeconomic Series, 27: 67-79. DOI: https://doi.org/10.1515/ bog-2015-0005
  • Grabowska, M. (2010). Wodne bariery rozwoju gospodarczego Polski (Water-related barriers in the economic development of Poland). Socio-Economic Problems and the State 1(3): 55-61. Available at: http://elartu.tntu.edu. ua/bitstream/123456789/648/1/SEPS_2010_v1_No3_M_
  • Grabowska-Water-related_barriers_in_the_economic_ development_of_Poland__55.pdf (3.12.2021).
  • Haque, M.M., Egodawatta, P., Rahman, A., & Goonetilleke, A. (2015a). Assessing the significance of climate and community factors on urban water demand. International Journal of Sustainable Built Environment, 4(2): 222- 230. DOI: https://doi.org/10.1016/j.ijsbe.2015.11.001
  • Haque, M., Rahman, A., Goonetilleke, A., Hagare, D., & Kibria, G. (2015b). Impact of climate change on urban water demand in future decades: An Australian case study. In: Daniels, J.A. (ed.) Advances in environmental research. United States of America: Nova Science Publishers, 57- 70. Available at: https://eprints.qut.edu.au/84018/21/ Impact of climate change on urban% 2Bwater demand in future decades_ An Australian case study.pdf (3.12.2021).
  • Hartigan, J.A. (1975). Clustering Algorithms. New York: John Wiley & Sons. Inc.
  • He, Y., Pang, Y., Zhang, Q., Jiao, Z., & Chen, Q. (2018). Comprehensive evaluation of regional clean energy development levels based on principal component analysis and rough set theory. Renewable Energy, 122: 643-653. DOI: https://doi.org/10.1016/j.renene.2018.02.028
  • Hellwege, J.N., Jeff, J.M., Wise, L.A., Gallagher, C.S., Wellons, M., Hartmann, K.E., Jones, S.F., Torstenson, E.S., Dickinson, S., Ruiz-Narváez, E.A., Rohland, N., Allen, A., Reich, D., Tandon, A., Pasaniuc, B., Mancuso, N., Im, H.K., Hinds, D.A., Palmer, J.R., Rosenberg, L., Denny, J.C., Roden, D.M., Stewart, E.A., Morton, C.C., Kenny, E.E., Edwards, T.L., & Velez Edwards, D.R. (2017). A multi-stage genome-wide association study of uterine fibroids in African Americans. Human Genetics, 136(10): 1363-1373. DOI: https://doi.org/10.1007/ s00439-017-1836-1
  • Hilman, Y., Rahim, A.A., Musa, M.H., & Hashim, A. (2007). Principal component analysis of factors determining phosphate rock dissolution on acid soils. Indonesian Journal of Agricultural Science, 8(1): 10-16. DOI: http://dx.doi.org/10.21082/ijas.v8n1.2007.p10-16
  • HOGC. 2020. Head Office of Geodesy and Cartography. BDOT10k - Baza Danych Obiektów Topograficznych (Database of Topographic Objects), NMT - Numeryczny Model Terenu (Digital Terrain Model) and shaded model from: https://mapy.geoportal.gov.pl/imap/Imgp_2. html (November 2020).
  • PRG - Państwowy Rejestr Granic i Powierzchni Jednostek Podziałów Terytorialnych Kraju (State Register of Borders and Area of Territorial Units of the Country) from: http://www.gugik.gov.pl/pzgik/inne-dane-udostepniane- bezplatnie (November 2020).
  • Hotloś, H. (2013). Analiza wpływu czynników meteorologicznych na zmienność poboru wody w miejskim systemie wodociągowym (Analysis of influence of meteorological factors on water demand variations in municipal water supply system- in Polish). Ochrona Środowiska, 35(2): 57-62. Available at: http://yadda. icm.edu.pl/baztech/element/bwmeta1.element.baztech- 07b84ec1-a4d4-4920-abdc-1f3250257942 (3.12.2021).
  • Hutcheson, G.D., & Sofroniou, N. (1999). The multivariate social scientist: Introductory statistics using generalized linear models. London-Thousand Oaks-New Dehli: SAGE Publications Ltd.
  • IBM Knowledge Center. 2018. Available at: https://www. ibm.com/support/knowledgecenter/pl/SSLVMB_sub/ statistics_mainhelp_ddita/spss/base/idh_quic.html (18.11.2018)
  • Iwamori, H., Yoshida, K., Nakamura, H., Kuwatani, T., Hamada, M., Haraguchi, S., & Ueki, K. (2017). Classification of geochemical data based on multivariate statistical analyses: Complementary roles of cluster, principal component, and independent component analyses. Geochemistry, Geophysics, Geosystems, 18(3): 994-1012. DOI: https://doi.org/10.1002/2016GC006663
  • Jankowska, J., Radzka, E., & Rymuza, K. (2017). Principal Component Analysis and Cluster Analysis In Multivariate Assessment of Water Quality. Journal of Ecological Engineering, 18(2): 92-96. DOI: https://doi. org/10.12911/22998993/68141
  • Jiang, Y., Guo, H., Jia, Y., Cao, Y., & Hu, C. (2015). Principal component analysis and hierarchical cluster analyses of arsenic groundwater geochemistry in the Hetao basin, Inner Mongolia. Geochemistry, 75(2): 197- 205. DOI: https://doi.org/10.1016/j.chemer.2014.12.002
  • Jolliffe, I. (2002). Principal Component Analysis. 2nd Edition. New York: Springer.
  • Jolliffe, I.T., & Cadima, J. (2016). Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374 (2065): 20150202. DOI: https://doi.org/10.1098/rsta.2015.0202
  • Kaiser, H.F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1): 141-151. DOI: https://doi. org/10.1177/001316446002000116
  • Khadra, C., Le May, S., Ballard, A., Théroux, J., Charette, S., Villeneuve, E., Parent, S., Tsimicalis, A., & MacLaren, Chorney J. (2017). Validation of the scale on Satisfaction of Adolescents with Postoperative pain management - idiopathic Scoliosis (SAP-S). Journal of Pain Research,10: 137-143. DOI: https://doi.org/10.2147/ JPR.S124365
  • Kolendo, M. (2016). Czynniki ekonomiczno-środowiskowe w modelowaniu miesięcznego zapotrzebowania na wodę na przykładzie Białegostoku (Economic and environmental aspects in modeling monthly water demand: the case study of Bialystok - in Polish). Ekonomia i Środowisko, 2(57): 162-174. Available at: http://yadda.icm.edu.pl/ baztech/element/bwmeta1.element.baztech-d75cfd59- 7213-4d11-9f1e-1c436997f941 (3.12.2021).
  • Krzanowski, W.J. (1992). Ranking principal components to reflect group structure. Journal of Chemometrics, 6(2): 97-102. DOI: https://doi.org/10.1002/cem.1180060207
  • Lindsey, C.R., Neupane, G., Spycher, N., Fairley, J.P., Dobson, P., Wood, T., McLing, T., & Conrad, M. (2018). Cluster analysis as a tool for evaluating the exploration potential of Known Geothermal Resource Areas. Geothermics, 72: 358-370. DOI: https://doi. org/10.1016/j.geothermics.2017.12.009
  • Marín Celestino, A.E., Martínez Cruz, D.A., Otazo Sánchez, E.M,. Gavi Reyes, F., & Vásquez Soto, D. (2018). Groundwater Quality Assessment: An Improved Approach to K-Means Clustering, Principal Component Analysis and Spatial Analysis: A Case Study. Water, 10(4): 437. DOI: https://doi.org/10.3390/w10040437
  • Moraetis, D., Lydakis-Simantiris, N., Pentari, D., Manoutsoglou, E., Apostolaki, C., & Perdikatsis, V. (2016). Chemical and physical characteristics in uncultivated soils with different lithology in semiarid Mediterranean clima. Applied and Environmental Soil Science, 2016: 3590548. DOI: https://doi. org/10.1155/2016/3590548
  • Nosvelli, M., & Musolesi, A. (2009). Water consumption and long-run socio-economic development: An intervention and a principal component analysis for the city of Milan. Environmental Modeling & Assessment, 14(3): 303-314. DOI: https://doi.org/10.1007/s10666-007-9127-1
  • Oketola, A.A., Adekolurejo, S.M., & Osibanjo, O. (2013). Water quality assessment of River Ogun using multivariate statistical techniques. Journal of Environmental Protection, 4(5): 466-479. DOI: http:// dx.doi.org/10.4236/jep.2013.45055
  • Otitoju, M.A., & Enete, A.A. (2016). Climate change adaptation: Uncovering constraints to the use of adaptation strategies among food crop farmers in Southwest, Nigeria using principal component analysis (PCA). Cogent Food & Agriculture, 2(1): 1178692. DOI: https:// doi.org/10.1080/23311932.2016.1178692
  • Panek, T., & Zwierzchowski, J.K. (2013). Statystyczne metody wielowymiarowej analizy porównawczej: teoria i zastosowania (Statistical methods of multivariate comparative analysis: theory and applications - in Polish). Warszawa: Oficyna Wydawnicza, Szkoła Główna Handlowa.
  • Lewandowska, A., & Piasecki A. (2019). Selected aspects of water and sewage management in Poland in the context of sustainable urban development. Bulletin of Geography. Socio-economic Series, 45(45): 149-157. DOI: https://doi.org/10.2478/bog-2019-0030
  • Piasecki, A., & Górski, Ł. (2018). Analysis of water consumption in 2014-2017 in Toruń. Infrastructure and Ecology of Rural Areas, IV/1/2018: 973-984. DOI: https://doi.org/10.14597/INFRAECO.2018.4.1.067
  • Piasecki, A., Jurasz, J., & Kaźmierczak, B. (2018). Forecasting Daily Water Consumption: a Case Study in Torun, Poland. Periodica Polytechnica Civil Engineering, 62(3): 818-824. DOI: https://doi.org/10.3311/PPci.11930
  • Piasecki, A., & Marszelewski, W. (2016). Development of Water and Sewage Infrastructure in Poland During the Cooperation with the EU. Wasserwirtschaft, 106(4): 34- 38.
  • Peltier, C., Visalli, M., & Schlich, P. (2015). Comparison of canonical variate analysis and principal component analysis on 422 descriptive sensory studies. Food Quality and Preference, 40 Part B: 326-333. DOI: https://doi. org/10.1016/j.foodqual.2014.05.005
  • Săndică, A.M., Dudian, M., & Ştefănescu, A. (2018). Air Pollution and Human Development in Europe: A New Index Using Principal Component Analysis. Sustainability, 10(2): 312. DOI: https://doi.org/10.3390/ su10020312
  • Sarker, R.C., Gato-Trinidad, S., & Imteaz, M. (2013). Temperature and rainfall thresholds corresponding to water consumption in Greater Melbourne, Australia. 20th International Congress on Modelling and Simulation (MODSIM2013), 2576-2582. Available at: https:// researchbank.swinburne.edu.au/file/4a3e1173-0254- 4ae9-b086-656a525311bd/1/PDF Published version .pdf (3.12.2021)
  • Schleich, J., & Hillenbrand, T. (2009). Determinants of residential water demand in Germany. Ecological Economics, 68(6): 1756-1769. DOI: https://doi. org/10.1016/j.ecolecon.2008.11.012
  • Stanisz, A.(2007). Przystępny kurs statystyki: z zastosowaniem STATISTICA PL na przykładach z medycyny. Analizy wielowymiarowe (An affordable statistics course: with the use of STATISTICA PL on examples from medicine. Multivariate analyzes - in Polish). Kraków: StatSoft Polska.
  • Thalib, L., Kitching, R.L., & Bhatti, M.I. (1999). Principal component analysis for grouped data-a case study. Environmetrics: The official journal of the International Environmetrics Society, 10(5): 565-574. DOI: https://doi.org/10.1002/ (SICI)1099-095X(199909/10)10:5 565::AIDENV360% 3E3.0.CO;2-R
  • Vajčnerová, I., Šácha, J., Ryglová, K., & Žiaran, P. (2016). Using the cluster analysis and the principal component analysis in evaluating the quality of a destination. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 64(2): 677-682. DOI: http://dx.doi. org/10.11118/actaun201664020677
  • Wentz, E.A., Wills, A.J., Kim, W.K., Myint, S.W., Gober, P., & Balling Jr, R.C. (2014). Factors influencing water consumption in multifamily housing in Tempe, Arizona. The Professional Geographer, 66(3): 501-510. DOI: https://doi.org/10.1080/00330124.2013.805627
  • Wright, C.J., Sargeant, J.M., Edge, V.L., Ford, J.D., Farahbakhsh, K., Shiwak, I., Flowers, C., Gordon, A.C., RICG., IHACC Research Team., & Harper, S.L. (2018). How are perceptions associated with water consumption in Canadian Inuit? A cross-sectional survey in Rigolet, Labrador. Science of the Total Environment, 618: 369-378. DOI: https://doi.org/10.1016/j.scitotenv.2017.10.255
  • WWAP (United Nations World Water Assessment Programme)/UN-Water. 2018. The United Nations World Water Development Report 2018: Nature-Based Solutions for Water. Paris, UNESCO.
  • Xenochristou, M., Blokker, M., Vertommen, I., Urbanus, J.F.X., & Kapelan, Z. (2018). Investigating the Influence of Weather on Water Consumption: A Dutch Case Study. WDSA / CCWI Joint Conference 2018, 1. Available at: https://ojs.library.queensu.ca/index.php/wdsa-ccw/ article/view/12048 (3.12.2021)
  • Xhafaj, E., & Nurja, I. (2015). The principal components analysis and cluster analysis as tools for the estimation of poverty, an Albanian Case Study. International Journal of Science and Research, 4(1): 1240-1243. Available at: https://www.ijsr.net/archive/v4i1/SUB15305.pdf (3.12.2021)
  • Xiao-jun, W., Jian-yun, Z., Shamsuddin, S., Rui-min, H., Xing-hui, X., & Xin-li, M. (2015). Potential impact of climate change on future water demand in Yulin city, Northwest China. Mitigation and Adaptation Strategies for Global Change, 20(1): 1-19. DOI: https://doi. org/10.1007/s11027-013-9476-9
  • Zhang, Y., Li, X., Mao, L., Zhang, M., Li, K., Zheng, Y., Cui, W., Yin, H., He, Y., & Jing, M. (2018). Factors affecting medication adherence in community-managed patients with hypertension based on the principal component analysis: evidence from Xinjiang, China. Patient Preference and Adherence, 12: 803-812. DOI: https://dx.doi.org/10.2147 PPA.S158662
  • Zhou, D., Zhang, R., Liu, L., Gao, L., & Cai, S. (2009). A study on water resources consumption by principal component analysis in Qingtongxia irrigation areas of Yinchuan Plain, China. Journal of Food, Agriculture & Environment, 7(3-4): 734-738.
  • Zou, H., Zou, Z., & Wang, X. (2015). An enhanced K-means algorithm for water quality analysis of The Haihe River in China. International Journal of Environmental Research and Public Health, 12(11): 14400-14413. DOI: https://doi.org/10.3390/ijerph121114400
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171636034

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.