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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. i 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, br. 12, 2023. https://doi.org/10.3390/toxics11120996

Kovačević, Miljan, Bahman Jabbarian Amiri, Silva Lozančić, Marijana Hadzima Nyarko, Dorin Radu i Emmanuel Karlo Nyarko. "Application of Machine Learning in Modeling the Relationship between Catchment Attributes and Instream Water Quality in Data-Scarce Regions." Toxics 11, br. 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. [pristupljeno 30.01.2025.];11(12). doi: 10.3390/toxics11120996

M. Kovačević, B. J. Amiri, S. Lozančić, M. Hadzima Nyarko, D. Radu i 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, br. 12, 2023. [Online]. Dostupno na: https://urn.nsk.hr/urn:nbn:hr:133:202903. [Citirano: 30.01.2025.]

Za citiranje koristite ovu mrežnu adresu: https://urn.nsk.hr/urn:nbn:hr:133:202903
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Podaci o radu
Naslov (engleski)Application of Machine Learning in Modeling the Relationship between Catchment Attributes and Instream Water Quality in Data-Scarce Regions
AutorMiljan Kovačević
AutorBahman Jabbarian Amiri
AutorSilva Lozančić
AutorMarijana Hadzima-Nyarko
AutorDorin Radu
AutorEmmanuel Karlo Nyarko
Autorova ustanovaSveučilište Josipa Jurja Strossmayera u Osijeku
Građevinski i arhitektonski fakultet Osijek
Znanstveno / umjetničko
područje, polje i grana		TEHNIČKE ZNANOSTI
Građevinarstvo
Sažetak (engleski)
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.
Ključne riječi (engleski)
Jezikengleski
Vrsta publikacijeZnanstveni rad - Izvorni znanstveni rad
Status objaveObjavljen
Vrsta recenzijeRecenziran - međunarodna recenzija
Verzija publikacijeObjavljena verzija rada (izdavačev PDF)
Naslov časopisaToxics
Brojčani podacivol. 11, br. 12, 996
e-ISSN2305-6304
DOIhttps://doi.org/10.3390/toxics11120996
URN:NBNurn:nbn:hr:133:202903
Datum objave publikacije2023
URL dokumentahttps://www.mdpi.com/2305-6304/11/12/996
Vrsta resursaTekst
Prava pristupaOtvoreni pristup
Uvjeti korištenjaIkona licence Imenovanje-Dijeli pod istim uvjetima 4.0 međunarodna (CC BY-SA 4.0)
Datum i vrijeme pohrane2025-01-22 13:25:26