Thesis (M.Sc.)-Cairo University, 2023.

Bibliography: pages 80-89.

Water quality assessment is crucial for understanding the changes in aquatic ecosystems and protecting human health. Analyzing the big data sets available for water quality over time using advanced statistical techniques like Functional Data Analysis (FDA) proved useful for evaluating rivers water quality. Following from this, this thesis focuses on using a range of FDA techniques to investigate and evaluate the water quality trends along the Nile River in Egypt, through identifying the different groups of water quality patterns along the river. Monthly physico-chemical data collected from multiple monitoring sites along the river between 2004 and 2013 were analyzed to identify high-risk areas and facilitate effective pollution mitigation strategies.
This thesis, specifically, employs functional principal components analysis (FPCA) to explore the sources of variations in the water quality trends between the monitoring sites. Next, different functional clustering techniques are applied to identify groups of sites with similar temporal trends in their water quality variates. The results of traditional functional clustering assuming independence are compared to the results of functional clustering that incorporates spatial dependence and flow connectedness.
The results of functional data analysis contribute to the existing knowledge by providing valuable insights into the spatial and temporal patterns of water quality along the Nile River. These findings have important implications for decision-making processes aimed at managing and improving water quality in the Nile River. The results of the functional clustering identified high-risk stations in Al-Gharbia and Aswan governorates. This highlights the urgent need for increased monitoring and the implementation of strict laws and regulations to mitigate pollution in these regions.

Issues also as CD.

Text in English and abstract in Arabic & English.