Particle Swarm Optimization Factors Evaluation in Para-tank Model Using Grey Decision-Making Method

• Main Authors: Hsu, Po-Yuan, 許博淵
• Other Authors: Yeh, Yi-Lung
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/11257637606735298588

博士 === 國立屏東科技大學 === 土木工程系所 === 104 === The relationship between rainfall and runoff has been the most important part of hydrological analysis. It is easier to get rainfall than to get runoff; therefore, the methods of calibration analysis regarding their relationship were actively developed in previous research to simulate the runoff mechanisms with rainfall data. In this study, the concept of tank model mechanism serves as a starting point to convert the original basic type of four tank sections into a combination of aboveground and underground mechanisms to address a higher proportion of impermeable layer in cities. A concept similar to a tank is used to establish the operational mechanisms produced after rainfall on the surface. It is simulated in the underground sewer system after overland flow, and therefore, a new rainfall–runoff model is established, called the Para-Tank Model (PTM). The particle swarm optimization (PSO) is employed to calculate the parameter values, including infiltration and depression head, sewer system head, terrain flooding feature outflow rat and sewer carrying capacity outflow rate, required by PTM. We also investigate three factors of the acceleration equation, i.e., acceleration constants c1 and c2 and inertia weight w, which are then used as events in PSO for parameter optimization in PTM during rainfall–runoff simulation. With Grey Situation Decision-Making, the values of 0.2, 0.5, and 0.8 are respectively used to create 27 groups of situation sets using the indices of the four objectives, root mean squared error, coefficient of efficiency, percent error of total volume, and squared value of flow error, in order to analyze the systematic effectiveness. After comparing the comprehensive effect measures, an optimal decision is reached when the combined effectiveness was at the highest when c1 = 0.8, c2 = 0.2, and w = 0.5 and becomes the optimal parameter value for the PTM.