Application of Back Propagation Artificial Neural Network to Real Time Analysis and Prediction of the Total Suspended Solids in Northern Taiwan Reservoirs

• Main Authors: Yu-Lin Chang, 張郁麟
• Other Authors: Jen-Chen Fan
• Format: Others
• Language: zh-TW
• Published: 2007
• Online Access: http://ndltd.ncl.edu.tw/handle/54218771155136620127

碩士 === 國立臺灣大學 === 生物環境系統工程學研究所 === 95 === In the management of reservoir, non-point source pollutions caused by surface soil erosion are frequently encountered. In order to prevent this kind of problems, it is necessary to continually monitor the watershed of the reservoir as well as to real-time monitor the total suspended solid(TSS). The data of the water quality of Xin-Shan reservoir, Feitsui reservoir, Shimen reservoir, Baoshan reservoir, Yonghe-Shan reservoir, and Mingd reservoir used in the study were provided by Environmental Protection Administration of the Executive Yuan, R.O.C.. These data included electrical conductivity, dissolved oxygen, pH value, turbidity, temperature, month, chlorophyll-α, total phosphorus, total hardness, and transmissivity, in the period from 1993 to 2005. Suitable water quality parameters and observation stations were further chosen from the statistical results by cluster analysis of the water quality, dominance analysis of the observation stations, and correlation coefficient of the reservoirs. Back propagation artificial neural network was applied to real time analysis and prediction of the total suspended solids. However the estimation accuracy would vary with locations and soil types. From the results, it was also found that the nural network model may be used to estimate the concentration of suspended solids, which is difficult to be real time measured, by using several parameters of water quality, which are easier to be measured, under some specific conditions. When back propagation network was modified to predict the real time total suspended solids in Shimen reservoir, the results showed that the predicted variation tendency of total suspended solids in network output agrees well with that in expected output, the R2 can reach 0.63, the regression coefficient can reach 0.90. It could be concluded that the method of back propagation artificial neural network and water quality can be used to rapidly and accurately estimate TSS.