Ecologia Balkanica

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https://doi.uni-plovdiv.bg/handle/store/4
p-ISSN: 1314-0213 / e-ISSN: 1313-9940

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Browsing Ecologia Balkanica by Author "Bekteshi, Lirim"

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    Evaluating heavy metal pollution and health risks in river systems using Random Forest and XGBoost: Evidence from the Shkumbin River
    (Plovdiv University Press "Paisii Hilendarski", 2025-12-19) Shyti, Bederiana; Basha, Lule; Bekteshi, Lirim
    Surface water contamination by heavy metals poses significant ecological and health risks due to their persistence, bioaccumulation, and toxicity. This research evaluated the concentrations of cadmium (Cd), chromium (Cr), copper (Cu), iron (Fe), lead (Pb), and zinc (Zn) in river water samples and assessed their impact on the Heavy Metal Pollution Index (HPI). Descriptive statistics revealed substantial variation among sampling sites, with HPI values ranging from 2.15 to 21.94. Although Cd and Pb were generally present in low concentrations, their localized maxima indicated potential hot spots of contamination, whereas Fe and Zn showed higher overall levels. To identify the most influential predictors of HPI, two machine learning regression models, Random Forest (RF) and Extreme Gradient Boosting (XGBoost), were implemented. The RF model explained more than 90% of the variance in HPI, with Cd, Zn, and Cr emerging as the most critical contributors. The XGBoost model achieved even higher predictive accuracy (R² = 0.998, RMSE = 0.76), confirming Cd and Cr as dominant predictors, together accounting for nearly 80% of the model’s explanatory power. These findings highlight the pivotal role of Cd and Cr in shaping HPI dynamics and demonstrate the utility of ensemble learning methods for environmental monitoring and risk assessment.
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    Remodeling of the WQI Index for the evaluation of the Shkumbini River’s water quality in Albania using the statistical method
    (Plovdiv University Press "Paisii Hilendarski", 2024-02-12) Shyti, Bederiana; Bekteshi, Lirim; Paralloj, Silvana; Hila, Erleta
    Nowadays, assessing the extent of natural water resources and making an accurate assessment of its quality is a task as important as it is urgent. The determination and assessment of the Water Quality Index (WQI) is a method used in our paper to assess the water quality of the Shkumbini River in Albania - one of the main rivers of our country. We used the analysis of the WQI index, because it is the most optimal analysis we can do with the collected data and because it is an easier index to interpret. WQI is considered fundamental information for analyzing and demonstrating the water quality. We have used the data collected during a 4-year period from the Shkumbini River’s water (in total 72 sets of data) and through the statistical method of Multiple Linear Regression (MLR) we have defined an equation for the assessment of WQI expressed by three variables: biological oxygen demand, hydrogen carbonate and phosphorus (BOD, HCO3, P-total). MLR is implemented by interpreting the correlation coefficient R2, which demonstrates that 99.6% variability of the data is explained by the new regression equation. The t-test was also used, demonstrating the equivalence of the WQI index, calculated from the newly constructed equation, and the values of the first WQI, calculated using nine variables. The reduction of the number of determining variables of WQI has its advantages, both financially, as well as from the way that the water quality of the Shkumbini River is interpreted and monitored.