prikaz prve stranice dokumenta Application of Machine Learning in Modeling the Relationship between Catchment Attributes and Instream Water Quality in Data-Scarce Regions
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Scientific paper - Original scientific paper
Application of Machine Learning in Modeling the Relationship between Catchment Attributes and Instream Water Quality in Data-Scarce Regions
Toxics, 11 (2023), 12; 996. https://doi.org/10.3390/toxics11120996
Kovačević, Miljan; Amiri, Bahman Jabbarian; Lozančić, Silva; Hadzima-Nyarko, Marijana; Radu, Dorin; Nyarko, Emmanuel Karlo

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Kovačević, M., Amiri, B. J., Lozančić, S., Hadzima Nyarko, M., Radu, D. & Nyarko, E. K. (2023). Application of Machine Learning in Modeling the Relationship between Catchment Attributes and Instream Water Quality in Data-Scarce Regions. Toxics, 11. (12). doi: 10.3390/toxics11120996

Kovačević, Miljan, et al. "Application of Machine Learning in Modeling the Relationship between Catchment Attributes and Instream Water Quality in Data-Scarce Regions." Toxics, vol. 11, no. 12, 2023. https://doi.org/10.3390/toxics11120996

Kovačević, Miljan, Bahman Jabbarian Amiri, Silva Lozančić, Marijana Hadzima Nyarko, Dorin Radu and Emmanuel Karlo Nyarko. "Application of Machine Learning in Modeling the Relationship between Catchment Attributes and Instream Water Quality in Data-Scarce Regions." Toxics 11, no. 12 (2023). https://doi.org/10.3390/toxics11120996

Kovačević, M., et al. (2023) 'Application of Machine Learning in Modeling the Relationship between Catchment Attributes and Instream Water Quality in Data-Scarce Regions', Toxics, 11(12). doi: 10.3390/toxics11120996

Kovačević M, Amiri BJ, Lozančić S, Hadzima Nyarko M, Radu D, Nyarko EK. Application of Machine Learning in Modeling the Relationship between Catchment Attributes and Instream Water Quality in Data-Scarce Regions. Toxics [Internet]. 2023 [cited 2025 January 30];11(12). doi: 10.3390/toxics11120996

M. Kovačević, B. J. Amiri, S. Lozančić, M. Hadzima Nyarko, D. Radu and E. K. Nyarko, "Application of Machine Learning in Modeling the Relationship between Catchment Attributes and Instream Water Quality in Data-Scarce Regions", Toxics, vol. 11, no. 12, 2023. [Online]. Available at: https://urn.nsk.hr/urn:nbn:hr:133:202903. [Accessed: 30 January 2025]

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Metadata
Title (english)Application of Machine Learning in Modeling the Relationship between Catchment Attributes and Instream Water Quality in Data-Scarce Regions
AuthorMiljan Kovačević
AuthorBahman Jabbarian Amiri
AuthorSilva Lozančić
AuthorMarijana Hadzima-Nyarko
AuthorDorin Radu
AuthorEmmanuel Karlo Nyarko
Author's institutionJosip Juraj Strossmayer University of Osijek
Faculty of Civil Engineering and Architecture Osijek
Scientific / art field,
discipline and subdiscipline		TECHNICAL SCIENCES
Civil Engineering
Abstract (english)
This research delves into the efficacy of machine learning models in predicting water quality parameters within a catchment area, focusing on unraveling the significance of individual input variables. In order to manage water quality, it is necessary to determine the relationship between the physical attributes of the catchment, such as geological permeability and hydrologic soil groups, and in-stream water quality parameters. Water quality data were acquired from the Iran Water Resource Management Company (WRMC) through monthly sampling. For statistical analysis, the study utilized 5-year means (1998–2002) of water quality data. A total of 88 final stations were included in the analysis. Using machine learning methods, the paper gives relations for 11 in-stream water quality parameters: Sodium Adsorption Ratio (SAR), Na+, Mg2+, Ca2+, SO42−, Cl−, HCO3−, K+, pH, conductivity (EC), and Total Dissolved Solids (TDS). To comprehensively evaluate model performance, the study employs diverse metrics, including Pearson’s Linear Correlation Coefficient (R) and the mean absolute percentage error (MAPE). Notably, the Random Forest (RF) model emerges as the standout model across various water parameters. Integrating research outcomes enables targeted strategies for fostering environmental sustainability, contributing to the broader goal of cultivating resilient water ecosystems. As a practical pathway toward achieving a delicate balance between human activities and environmental preservation, this research actively contributes to sustainable water ecosystems.
Keywords (english)
Languageenglish
Publication typeScientific paper - Original scientific paper
Publication statusPublished
Peer reviewPeer review - international
Publication versionPublished version
Journal titleToxics
Numberingvol. 11, no. 12, 996
e-ISSN2305-6304
DOIhttps://doi.org/10.3390/toxics11120996
URN:NBNurn:nbn:hr:133:202903
Publication2023
Document URLhttps://www.mdpi.com/2305-6304/11/12/996
Type of resourceText
Access conditionsOpen access
Terms of useLicence Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) icon
Created on2025-01-22 13:25:26