@ARTICLE{Ahmedi_Figene_The_2023,
 author={Ahmedi, Figene and Makolli, Shkumbin},
 number={No 59},
 pages={8-12},
 journal={Journal of Water and Land Development},
 howpublished={online},
 year={2023},
 publisher={Polish Academy of Sciences; Institute of Technology and Life Sciences - National Research Institute},
 abstract={In this paper, the regression analysis technique is applied to a large water quality dataset for the Sitnica River in Kosovo. It has been done to assess the correlation between water quality parameters. The data are generated by a wireless sensors network deployed in Sitnica. A regression analysis is applied to four water quality parameters: temperature, dissolved oxygen, pH, and electrical conductivity. The correlation between each pair of parameters has been assessed by using the WEKA software package, which is a popular time-saving tool for data analysis in distinct domains. The data are pre-processed to exclude out-of-range values and then the assessment of correlation for the pairs of parameters is applied. In comparison to other pairs of water quality parameters, the results show that dissolved oxygen and electrical conductivity correlate particularly closely with temperature. Regression equations of these two pairs of parameters may provide inferred information on dissolved oxygen and electrical conductivity about the Sitnica River. Such information may otherwise not be available to resource managers in Kosovo. Moreover, due to its easy to use and availability as an open-source software, WEKA may aid decision-makers on the management providing almost real-time information about surface water quality within the basin. This can be particularly useful especially in the case of continuous observation of water quality and a huge dataset gathered by using wireless sensors.},
 type={Article},
 title={The correlation of water quality parameters over wireless sensors generated dataset in the Sitnica River in Kosovo},
 URL={http://ochroma.man.poznan.pl/Content/129971/PDF/2023-04-JWLD-02.pdf},
 doi={10.24425/jwld.2023.147223},
 keywords={monitoring, parameters’ pair, regression analysis, water quality, wireless sensors},
}