Prediction of Delivered Quantities of Drinking Water and Discharged Wastewater of the Nišava District (Serbia)
| Autor: | Nina Pavićević |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Polynomial regression
Polynomial regression model business.industry Water Delivered quantities of drinking water 030209 endocrinology & metabolism Regression analysis General Medicine 6. Clean water 03 medical and health sciences Trend analysis 0302 clinical medicine Wastewater Linear regression Statistics Medicine 030212 general & internal medicine Natural resources Total discharged wastewater Quadratic regression model business |
| Zdroj: | Open Access Macedonian Journal of Medical Sciences; Vol. 8 No. E (2020): E-Public Health; 664-669 |
| ISSN: | 1857-9655 |
| DOI: | 10.3889/oamjms.2020.5561 |
| Popis: | Water, as a natural resource, is the most basic substance of life that has immeasurable significance for the living world, ecosystems, and planet earth. In this paper, a prediction of delivered quantities of drinking water (DQDW) and total discharged wastewater (TDWW) of the Nišava district (Serbia) for the period 2019-2023 is given. The prediction for DQDW for the period 2019-2023 was made based on linear regression model, quadratic regression model, and cubic regression model according to which the data on DQDW of the Nišava district (Serbia) for the period 2006-2018 were approximated. The prediction for TDWW for the period 2019–2023 was done based on the 4th-degree polynomial regression model, the 5th-degree polynomial regression model, and the 6th-degree polynomial regression model by which the DQDW data were approximated of the Nišava district (Serbia) for the period 2006–2018. The presented prediction is a continuation of the paper “Trend analysis of total affected water and total discharged wastewater of the Nišava district (Serbia)” by the same author, in which for data on DQDW and TDWW of the Nišava district (Serbia) for the period 2006–2018 trend analysis and selected regression models have been shown. |
| Databáze: | OpenAIRE |
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