Comparison of Frequency Analysis with Density Based Clustering in Evaluating Extreme Streamflows

2018-12-31
Analysis of streamflow data has significant importance concerning sustainable use of water resources, planning supply of short and long term demands. Streamflow, unlike many other completely random variables, is a dependent variable which is closely related to both meteorological (such as precipitation, temperature) and catchment characteristics (such as land use and soil type). Thus inter-dependencies should be taken into consideration while analyzing streamflow. Besides this, while analyzing streamflow data, outliers which can be defined as a data point which has very different features from the rest of the data and describes abnormal behavior of a system of data set should be taken into account (Aggarwal 2001). Traditionally, analysis of streamflow data is done by frequency analysis which estimates the probability of occurrences of specified events, such as low, high flows or flows for various return periods. In this project, alternative to the traditional frequency analysis, innovative and computationally effective density based clustering method is used to analyze streamflow data. The aim of this project is to evaluate results of traditional frequency analysis of streamflow in comparison to streamflow data analysis based on density based clustering. As a result of this study, the differences and similarities in the results of these two approaches, their relative advantages and disadvantages will be identified. Proposed methods will be applied at a case study catchment in

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Citation Formats
E. Kentel Erdoğan, “Comparison of Frequency Analysis with Density Based Clustering in Evaluating Extreme Streamflows,” 2018. Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/59084.