Constructing the suspended sediment concentration model in Shihmen Reservoir watershed by artificial neural networks
Date Issued
2011
Date
2011
Author(s)
Chen, Yi-Hung
Abstract
High concentration of sediment in upstream river is one of the important issues that affect the effectiveness of water resources management and reservoir operations in watersheds during typhoon periods. The measurement of sediment in the river is difficult to continuously and effectively achieve because of time- and human- consuming. Furthermore, traditional methods such as regression analysis and sediment rating curve are not able to provide effective simulation of sediments. As a result, there is a necessity of establishing a precise model for suspended sediment estimation and prediction. The study can be divided into two topics, in which the first topic tries to construct the estimation of sediment concentration at Hsia-Yun gauging station by applying the Adaptive Network-Based Fuzzy Inference System (ANFIS). To analyze the relationship between hydrological variables and suspended sediment concentration, the daily streamflow, precipitation, and sediment concentration data recorded in the years of 1982-2009 were collected. The results not only showed that the best input combination of a model was consisted of current streamflow and the accumulated rainfall but also revealed that the performance obtained from ANFIS outperformed conventional methods in terms of model accuracy, when predicting daily suspended sediment concentrations.
The second topic focuses on the prediction of event-based suspended sediment concentration at Lo-Fu gauging station during typhoon periods using the hourly average rainfall, inflow rate, and suspended sediment concentration. By taking various input variables into account, the study successfully constructed a suspended sediment concentration prediction model by using ANFIS with the input dimensions consisting of antecedent one-hour inflow rate, antecedent one- and two-hour suspended sediment concentration, and antecedent seven-hour rainfall information. Overall, the study demonstrates that the performance obtained from ANFIS can be used as a reference to the management of reservoir operation and water discharge because typhoons always result in a high concentration of suspended sediment in both river and reservoir.
Subjects
Adapted Network-Based Fuzzy Inference System
Artificial Neural Network
Suspended Sediment Concentration
Estimation
Prediction
Typhoons
Type
thesis
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