Application of Artificial Neural Network to Estimate Dispersivity for Tracer Test in Two-Dimensional Radially Convergent Flow Field

• Main Authors: Hung-Yu Shieh, 謝宏育
• Other Authors: 劉振宇
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/31307566807557673583

碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 96 === Advection-dispersion equation (ADE) describes the solute transport process in saturated aquifer, the dispersivity is the main parameter of ADE. Traditionally, the use of type curve-fitting to estimate dispersivity by analyzing the field data generally requires to a large amount of time, and the analysis accuracy is difficult to control. This study applied the back propagation neural network (BPN) model to analyze two-dimensional radially convergent flow tracer tests. The developed back propagation neural network fitting model (BPNFM) incorporates the scale-dependent dispersivity model (SDM) to automatically estimate the longitudinal and transverse dispersivities as well as the effective porosity. The prediction errors of training and validation data show that the scale-dependent longitudinal dispersivity fitting model and the effective porosity fitting model can maintain the prediction errors within 2% while the Peclet number is between 0.5 to 100, the effective porosity is between 0.05 to 0.5, respectively. The scale-dependent transverse dispersivity fitting model can maintain the prediction errors within 5%, 8%, 10% and 20% while the scale-dependent transverse dispersivity is between 0.3 to 10 meters, 0.1 to 0.3 meters, 0.03 to 0.1 meters and 0.01 to 0.3 meters, respectively. Two field data were used to demonstrate the efficiency and accuracy of BPNFM. The BPNFM not only significantly reduces the analysis time but also yields accurate matching result by comparing to the manual type curve-fitting results. The developed BPNFM is an effective tool for analyzing the dispersivities of the field tracer tests.