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UBC Theses and Dissertations

Water resources data quality assessment and description of natural processes using artificial intelligence techniques Lauzon, Nicolas


The assessment of the quality of any data is difficult to perform if only because of the subjective nature of this task, where quality may be interpreted differently from one scientific domain to another, viewed differently in various cultures and societies, and considered as a more or less chronic problem depending on the context of application. Data, whether employed directly or as inputs for any data analysis or modeling efforts, are at the base of any decision-making process, and a characterisation of their quality is essential in determining bias in any decision on which they are based. This thesis focuses on the assessment of the quality of data regularly employed in water resources engineering and management, in particular hydrometric data and modeling parameters. New approaches are proposed for the detection of three types of anomalies, outliers, shifts and trends, which are a persistent concern to engineers and managers alike, and have been the focus of much research directed at reducing bias in the estimation of water quantity and quality. Artificial intelligence techniques (AITs) constitute the foundations of these new approaches, which are designed to take advantage of the capacity of AITs to provide representative descriptions of data domains. Based on theoretical experiments of their performance relative to conventional statistical diagnostics, and on applications to real hydrometric data from representative watersheds in Canada, the AIT-based approaches may indeed be used to confirm the results from conventional approaches as well as complement and, in some cases, enhance them. Since the ultimate use of hydrometric data is likely as inputs to hydrologic, hydraulic or water quality models, applications of AITs in the simulation of natural processes are also explored in this work. These applications focus on inflow and algae concentration modeling, and demonstrate that improvements in modeling estimations can be gained from the description of natural processes with AIT. Throughout this thesis, discussions regarding the advantages and disadvantages of these AIT-based approaches are provided along with suggestions for future developments.

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