Kovačević Miljan, Jabbarian Amiri Bahman, Lozančić Silva, Hadzima-Nyarko Marijana, Radu Dorin, Nyarko Emmanuel Karlo
Faculty of Technical Sciences, University of Pristina, Knjaza Milosa 7, 38220 Kosovska Mitrovica, Serbia.
Faculty of Economics and Sociology, Department of Regional Economics and the Environment, 3/5 P.O.W. Street, 90-255 Lodz, Poland.
Toxics. 2023 Dec 7;11(12):996. doi: 10.3390/toxics11120996.
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, Mg, Ca, SO, Cl, HCO, 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.
本研究深入探讨了机器学习模型在预测集水区水质参数方面的功效，重点在于揭示各个输入变量的重要性。为了管理水质，有必要确定集水区的物理属性（如地质渗透率和水文土壤组）与河流水质参数之间的关系。通过每月采样从伊朗水资源管理公司（WRMC）获取水质数据。为了进行统计分析，该研究使用了5年（1998 - 2002年）的水质数据均值。分析共纳入了88个最终站点。利用机器学习方法，本文给出了11个河流水质参数的关系：钠吸附比（SAR）、钠（Na）、镁（Mg）、钙（Ca）、硫酸根（SO）、氯（Cl）、碳酸氢根（HCO）、钾（K）、pH值、电导率（EC）和总溶解固体（TDS）。为了全面评估模型性能，该研究采用了多种指标，包括皮尔逊线性相关系数（R）和平均绝对百分比误差（MAPE）。值得注意的是，随机森林（RF）模型在各种水质参数方面表现突出。整合研究成果能够制定有针对性的策略以促进环境可持续性，为培育有韧性的水生态系统这一更广泛目标做出贡献。作为在人类活动与环境保护之间实现微妙平衡的实际途径，本研究为可持续水生态系统做出了积极贡献。
