Identification of Groundwater Quality by Statistical Methods and a Mathematical Method in the Khemisset–Tiflet Region

Abstract

Groundwater is one of the most important natural resources that is overexploited and extensively polluted by human activity. Furthermore, drinking this dirty water might have major consequences for human health. Before using groundwater, it is consequently required to conduct a precise and regular assessment of its quality. Furthermore, for five monitoring stations in the Khemisset-Tiflet region, cluster analysis (CA), principal component analysis (PCA), and a fuzzy logic technique were utilized to analyze water quality. The CA classified the sample sites into three categories. The PCA identified temporal characteristics of water quality status. Group I include stations characterized by high temperature and low DO, COD, and BOD5 values. Group II includes stations characterized by high values of pH and low concentrations of NO 3 -, Cl -, SO 4 2and turbidity. Group III includes stations characterized by high concentrations of NO 3 -, Cl -, SO 4 2and turbidity and low concentrations of pH. In addition, fuzzy logic to reveal more information about groundwater quality. In effect, water quality in spring and winter was the best; the parameters responsible for the deterioration of water quality are NO 3 -, Cl -, SO 4 2and turbidity.

Standardizing the Wastewater Composition in Order to Minimize the Eutrophication Risk for the Reservoir

“…The random nature of the value х for a given treatment facilities mode of operation is a consequence of a large number of random natural and technical factors. This circumstance, according to the central limit theorem (Elassassi Zahra et al, 2022), allows assuming that the value of х with a high degree of probability is distributed according to the normal law.…”