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http://dx.doi.org/10.5762/KAIS.2020.21.10.509

A Research on the Probabilistic Calculation Method of River Topographic Factors  

Choo, Yeon-Moon (Research Institute of Industrial Technology, Pusan National University)
Ma, Yun-Han (Dept. of Civil and Environmental Engineering, Pusan National University)
Park, Sang-Ho (Dept. of Civil and Environmental Engineering, Pusan National University)
Sue, Jong-Chal (Dept. of Civil and Environmental Engineering, Pusan National University)
Kim, Yoon-Ku (Dept. of Civil and Environmental Engineering, Pusan National University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.21, no.10, 2020 , pp. 509-516 More about this Journal
Abstract
Since the 1960s, many rivers have been polluted and destroyed due to river repair projects for economic development and the covering of small rivers due to urbanization. Many studies have analyzed rivers using measured river topographic factors, but surveying is not easy when the flow rate changes rapidly, such as during a flood. In addition, the previous research has been mainly about the cross section of a river, so information on the longitudinal profile is insufficient. This research used informational entropy theory to obtain an equation that can calculate the average river slope, river slope, and river longitudinal elevation for a river basin in real time. The applicability was analyzed through comparison with measured data of a river's characteristic factors obtained from a river plan. The parameters were calculated using informational entropy theory, nonlinear regression analysis, and actual data. The longitudinal elevation entropy equation for each stream was then calculated, and so was the average river slope. All of the values were over 0.96, so it seems that reliable results can be obtained when calculating river characteristic factors.
Keywords
Informational Entropy; Mean River Slope; River Slope; Nonlinear Regression Analysis; Longitudinal Elevation;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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