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Research on the Longitudinal Section of River Restoration Using Probabilistic Theory.
Choo, Yeon-Moon; Kim, Ji-Min; An, Ik-Tae.
Afiliação
  • Choo YM; Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Korea.
  • Kim JM; Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Korea.
  • An IT; Department of Civil and Environmental Engineering, Pusan National University, Busan 46241, Korea.
Entropy (Basel) ; 23(8)2021 Jul 27.
Article em En | MEDLINE | ID: mdl-34441105
ABSTRACT
Since the 1960s, many rivers have been destroyed as a consequence of the process of rapid urbanization. As accurate figures are important to repair rivers, there have been many research reports on methods to obtain the exact river slope and elevation. Until now, many research efforts have analyzed the river using measured river topographic factors, but when the flow velocity changes rapidly, such as during a flood, surveying is not easy; and due to cost, frequent measurements are difficult. Previous research has focused on the cross section of the river, so the information on the river longitudinal profile is insufficient. In this research, using informational entropy theory, equations are presented 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 a comparison with the measured data of river characteristic factors obtained from the river plan. The parameters were calculated using informational entropy theory and nonlinear regression analysis using actual data, and then the longitudinal elevation entropy equation for each river and the average river slope were calculated. As a result of analyzing the applicability of the equations presented in this study by R2 and Root Mean Square Error, all R2 values were over 0.80, while RMSE values were analyzed to be between 0.54 and 2.79. Valid results can be obtained by calculating river characteristic factors.
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Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Idioma: En Ano de publicação: 2021 Tipo de documento: Article