Application of Perturbance Moment Method to the prediction of suspended sediment concentration in Xindian river and its associated uncertainty

• Main Authors: Da-Wei Kao, 高大衛
• Other Authors: Christina_w_tsai
• Format: Others
• Language: zh-TW
• Published: 2018
• Online Access: http://ndltd.ncl.edu.tw/handle/n3j5t2

碩士 === 國立臺灣大學 === 土木工程學研究所 === 106 === The modeling of the concentration of suspended sediment necessarily involves with uncertainty. The main goal of this research is to predict the output concentration of suspended sediment in Xindian river in Taipei, Taiwan. Mike 21 Hydrodynamic Model (HD) and Mike 21 Sediment Transport (ST) model are utilized to the modeling, and the study area is focused on Xindian river watershed. First, under the boundary discharge and normal depth conditions, the output depth-averaged horizontal flow velocity can be obtained from the Hydrodynamic Model, then the output of Hydrodynamic Model input to ST to simulate the output concentration. Second, the historical stage-discharge rating curve data and concentration-discharge rating curve are used for calibration validation. The Manning number and dispersion coefficient are the main items to be calibrated. Finally, after calibration and validation, a flood design with 1000 cubic meter per second (cms) is adopted to predict the concentration using a state-of-the-art method for uncertainty analysis called the perturbance moment method (PMM). The PMM is more efficient than the Monte-Carlo simulation (MCS). In MCS, calculations may become cumbersome when they involve multiple uncertain parameters and variables. In the PMM, the entire probability distribution of a random variable is redistributed among three points., and the statistical moments (such as mean value and standard deviation) for the output can be presented by the representative points and perturbance moments based on the parallel axis theorem. With assumed independent parameters and variables, the computation time of the PMM is significantly lower than MCS for a comparable modeling accuracy. This research, takes natural and parameter uncertainty into account, with natural uncertainty grain size are considered since the grain size is the input data in the ST model and for the parameter uncertainty the Manning number and dispersion coefficient are considered since they are main parameters for the calibration in the HD model and ST model. After evaluating the moments of output suspended sediment concentration by PMM, the range of the suspended sediment concentration prediction will be obtained.