Proposing a novel method for the irrigation water quality assessment, using entropy weighted method, entitled: "EIWQI".

Autor: Gorgij, Alireza Docheshmeh, Moayeri, Mohammad Mehdi
Předmět:
Zdroj: Environmental Earth Sciences; Oct2023, Vol. 82 Issue 20, p1-12, 12p, 2 Color Photographs, 1 Diagram, 5 Charts, 1 Graph, 1 Map
Abstrakt: In the pursuit of advancing water quality assessment methodologies, particularly for the purposes of irrigation, it is of utmost importance to enhance our understanding of water quality to effectively manage water resources. Recognizing this necessity, our study introduces the application of the Entropy method, for the first time, in the assessment of irrigation water quality in the Urmia plain groundwater resources. The approach coined as entropy weighted irrigation water quality index (EIWQI) has been devised. Key indicators of irrigation water quality, namely, Sodium Absorption Ratio (SAR), Residual Sodium Carbonate (RSC), Sodium percentage (Na%), Total Hardness (TH), and Electrical Conductivity (EC), were calculated for 69 groundwater samples, alongside the determination of major ions present in the groundwater, including Ca2+, Mg2+, Na+, K+, So42, Cl, HCo3, and CO32−. Subsequently, the water quality index was quantified through the implementation of the entropy weighted method. The proposed EIWQI enables the classification of irrigation water quality into four categories: excellent, good, doubtful, and unsuitable. Following the application of the EIWQI to the aforementioned data points, the groundwater of the study area was categorized as good (42.02%), doubtful (52.17%), and unsuitable (5.79%). To assess the accuracy of the proposed methodology, the EIWQI calculation results were compared with the SAR, Na%, and RSC values obtained from the sample points, as well as their fuzzy overlay outputs. Performance criteria such as Mean Absolute Deviation (MAD), Mean Absolute Percentage Error (MAPE), Mean Square Error (MSE), and Root Mean Square Error (RMSE) were utilized, all of which substantiated the accuracy of the model, yielding values of 0.217, 8.574, 0.217, and 0.466, respectively. These results underscore the precision and applicability of the proposed methodology. [ABSTRACT FROM AUTHOR]
Databáze: Complementary Index