Title page for etd-0811117-214021


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URN etd-0811117-214021
Author Shih-ming Liu
Author's Email Address min640@hotmail.com.tw
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Department Environmental Engineering
Year 2016
Semester 2
Degree Master
Type of Document
Language zh-TW.Big5 Chinese
Title Influential factor analysis of total suspended particle concentration in Kaohsiung Harbor area using artificial neural network
Date of Defense 2017-07-18
Page Count 73
Keyword
  • Total Suspended Particle
  • Material Density
  • Relative Humidity
  • Kaohsiung Harbor
  • Neural Network
  • Abstract As nowadays the economic activities around the world interact frequently and closely, marine shipping is an important approach for international exchange of capitals, goods, and services. However, the associated air pollution is also becoming a serious environmental issue of concern.Kaohsiung harbor is the largest international port in southern Taiwan and plays an important role in the national economic development, whereas its long-term emissions of a variety of air pollutants such as total suspended particle (TSP) cargo loading and unloading is notorious environmental problem. The main sources for the air pollution in this areainclude the loading activities on a number of piers, while the materials to handle typically are composed of iron waste (scrap), coke, silica sand, ore, limestone and cement. The objective of this study is to investigate the feasibility of usingneural network system to predict the TSP concentrations from different piers and to study the important factors that influence the TSP emissions. The TSP concentrations analyzed from 2010 to 2012 were used as the database to study. The variables included material density, relative humidity, wind direction and wind speed, as the dependent variable was the TSP concentration. In the results of developing the neural network for learning,the mode 1, which separate the inputdata (for learning) and the check data (for demonstrating) by their sizes, helped the neural network generate more accurate results. The percentage of the relative error was the lowest (13%) when the hidden layer was three. By optimizing proper factors, the neural network established in this study was capable to provide reasonable predictions for the possible TSP concentrations on the piers in Kaohsiung Harbor by knowing the information including the material density, relative humidity, wind direction and wind speedoneach harbor. The major factors affecting the TSP concentration in the harbor includedthe material density and relative humidity. With properly known atmospheric characteristics such as those considered in this study, the neural network investigated in this study provides a preliminary result that can employed to decide whether it is necessary to strengthen the precautionary measures or to perform other strategies to curb the TSP concentrations in a harbor area such as Kaohsiung Harbor in this study。
    Advisory Committee
  • Shu-Kuang Ning - chair
  • Chun-Hu Chen - co-chair
  • Wei-Hsiang Chen - advisor
  • Files
  • etd-0811117-214021.pdf
  • Indicate in-campus at 99 year and off-campus access at 99 year.
    Date of Submission 2017-09-12

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