Using Back-Propagation Neural Network and Multiple Linear Regression to Analyze the Impact of Construction Sites on PM2.5 in Taichung County

• Main Authors: Chiao-Wan Huang, 黃皎椀
• Other Authors: Tzy-Yi Pai
• Format: Others
• Language: zh-TW
• Published: 2011
• Online Access: http://ndltd.ncl.edu.tw/handle/18200626934929765051

碩士 === 朝陽科技大學 === 環境工程與管理系碩士班 === 99 === This research uses Back-Propagation Neural Network（BNN）and Multiple Linear Regression（MLR）to establish construction sites’ air quality forecasting module in Taichung County. The variables are PM2.5, PM10, SRC, RC, tunnel constructions and other construction works. By using the optimized network established from data of January to November 2008, a forecast was produced using BNN and MLR for the result in 2008. By using BNN and MLR, this research have produced a simulated forecast for PM2.5 in Taichung County. Both BNN and MLR forecasting models have showed capabilities in capturing the changes and trend of the PM2.5 concentration level. At DaLi MLR outperformed BNN in forecasting results in DaLi, the forecasted relative factor is highest in DaLi（6V1）and ShaLu（6V1, 5V1, 4V1）at 0.47 and lowest at FongYuang（4V1, 3V1, 2V1）at 0.29. BNN outperformed MLR in forecasting results in FongYuan and ShaLu, the forecasted relative factor is highest in FongYuan（4V1, 4V1~2V1）at 0.94 and lowest in DaLi（4V1, 3V1）at DaLi. BNN’s relative training factor is between 0.73~0.95 whereas MLR’s relative testing factor is between 0.68~0.76. The relative factor of simulated forecast is between 0.4~0.94 for BNN and 0.29~0.47 for MLR, overall speaking BNN’s forecast is better than MLR.