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Developing a New Framework for Assessing the Impact of Data Uncertainty on Water Quality Index based on Multivariate Analysis

Jahangiri, Farshad | 2023

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 56127 (09)
  4. University: Sharif University of Technology
  5. Department: Civil Engineering
  6. Advisor(s): Sheikholeslami, Razi
  7. Abstract:
  8. Over the past several decades, human activities have resulted in a significant increase in surface and ground water pollution. Human activities have caused significant pollution of surface and groundwater, with pollutants like nitrates, phosphorus, heavy metals, toxins, and chemical fertilizers degrading water quality. In order to control pollution and reduce the concentration of pollutants in water, it is necessary to use appropriate numerical criteria to evaluate and manage the quality of water resources. The water quality index, as a scoring method for investigating the combined effects of independent parameters on overall water quality, is a widely-used metric for measuring the quality of water resources. Therefore, the calculation of this index, vulnerability assessment and the preparation of pollution risk maps are often done without considering uncertainty. Results cannot be reliable if this uncertainty is ignored, which can be significantly high in some cases. As a solution to this problem, the current research is developing a new water quality index that incorporates the uncertainty of the input data. Using uncertainty-aware principal component analysis, a new multivariate statistical method is developed for this purpose. This thesis proposes a method for assessing water quality that accounts for the uncertainty of input data and reduces the potential for expert bias. Moreover, the sensitivity analysis method is also utilized in the presented framework in order to identify the most important factors affecting the uncertainty of the water quality index. In order to employ the proposed approach in this thesis, a 9-year time series (from 2013 to 2021) corresponding to 16 water quality parameters (such as phosphorus, nitrogen, dissolved solids, etc.) was used in the Susquehanna watershed. The results showed that considering the uncertainty of data can alter the corresponding water quality status of some stations, which can lead to changes in the policies adopted for these stations. Furthermore, it was observed that using different methods in developing water quality indices can affect the degree and manner in which the input data uncertainty is reflected in the output uncertainty of the water quality index. The current study offers comprehensive and reliable information on water pollution, which can assist in making informed decisions to improve water quality in the study area. The findings of this research are expected to enable other data-driven environmental and hydrological assessments to analyze the level of uncertainty in their results using the proposed method. Consequently, this research can help to improve the accuracy and reliability of water quality assessment, leading to better decision-making and management practices for water resources
  9. Keywords:
  10. Quality Index ; Water Quality ; Quality Management ; Principal Component Analysis (PCA) ; Input Data Uncertainty ; Water Resources Management ; Water Pollution ; Multivariate Data Analysis

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