Water quality control, especially in large-scale monitoring regions or networks, requires easy and automatic processes for detecting potential outliers in a reproducible manner. This study focuses on removing outlier values from a dataset collected by an online monitoring station on the Yeşilırmak River between 2007 and 2009. Seven different parameters were evaluated: dissolved oxygen (luminescence dissolved oxygen, LDO), temperature, pH, conductivity, total organic carbon (TOC), nitrate nitrogen (NO3-N), and ammonium nitrogen (NH4-N). Five methods – median, mean, Grubbs’, generalized extreme studentized deviate (GESD), and interquartile range (IQR) – were used for outlier removal. The developed models were integrated into a graphical user interface (GUI) in the MATLAB environment, facilitating practical and easy access. This study enables users to input any dataset into the software and remove outlier values using various methods in a few steps, thus preparing the data for modeling studies. It was observed that the median algorithm removed the most data points among the outlier data-removal methods.
River water quality data outlier detection statistical methods graphical user interface (GUI)
Birincil Dil | İngilizce |
---|---|
Konular | Çevresel Değerlendirme ve İzleme |
Bölüm | Makaleler |
Yazarlar | |
Yayımlanma Tarihi | 30 Haziran 2024 |
Gönderilme Tarihi | 19 Aralık 2023 |
Kabul Tarihi | 11 Haziran 2024 |
Yayımlandığı Sayı | Yıl 2024 Cilt: 19 Sayı: 2 |
“Journal of International Environmental Application and Science”