PM10 IMPACT SIMULATION OF CONSTRUCTION SITES ON TAICHUNG CITY USING BACK-PROPAGATION NEURAL NETWORK (BNN) METHOD AND MULTIPLE LINEAR REGRESSION (MLR) METHOD

• Main Authors: Chih-Peng Su, 蘇志朋
• Other Authors: Tzu-Yi Pai
• Format: Others
• Language: zh-TW
• Online Access: http://ndltd.ncl.edu.tw/handle/71354350942085400773

碩士 === 朝陽科技大學 === 環境工程與管理系碩士班 === 98 === This study employed Back-Propagation Neural Network(BNN) method and Multiple Linear Regression (MLR) method to establish an air quality prediction model of Taichung city. We used PM10 as the variable factor and PM2.5, building constructions using reinforced concrete(RC), building constructions using Steel Reinforced Concerte(SRC), Building dismantling and tunnel constructions as input parameters in BNN method for an optimizing network. We inputted data from January to November 2008 as parameters, then used the model to predicted the air quality of December, 2008. The prediction result of BNN method was compared to MLR method. As best prediction result using BNN method of simulating for Taichung city. R value is 0.3.Mape value is 24.87%. As best prediction result using MLR method of simulating for Taichung city. R value is 0.7.Mape value is30.2%. As best prediction result using BNN method: R value is 0.3.Mape value is 24.87%. As best prediction result using MLR method: R value is 0.4.Mape value is 23.63%. separately. This study shows that these prediction models will be a supportive tool for air pollution management decision-making process of the authorities.