Application of Artificial Neural Network Model on Estimation of Carbon Dioxide Flux in Low Latitude Wetland Ecosystem

• Main Authors: Tzu-Yi Lu, 呂姿儀
• Other Authors: Jehn-Yih Juang
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/58085137110223941940

碩士 === 國立臺灣大學 === 地理環境資源學研究所 === 105 === 　　In wetland studies, few attentions have been given to low-latitude wetland ecosystem presently, but it accounts for about 70% of the global wetland area. Therefore, it’s very important to consider the contribution of this significant portion on global carbon (C) budget. In the past decades, eddy-covariance method has been widely applied in many C budget studies at the ecosystem scale, but there are still several limitations affecting the performance of EC methods. In order to overcome the abovementioned limitations, many linear or non-linear statistical techniques are applied to fill the measurement gap. Among various methods, the Artificial Neural Network (ANN) method is considered to be an excellent means to identify the complex non-linear relationship between the CO2 flux and meteorological variables. 　　In this study, a back-propagation ANN model was applied to quantify CO2 flux at three low-latitude wetland sites (Guandu Nature Park Tower One (GDP-T1), Guandu Nature Park Tower Two (GDP-T1), and Florida Everglades short hydroperiod marsh (US-Esm)) in East Asia and the US. Meteorological variables were used as the input parameters to train the ANN to predict the CO2 exchange. The best results of the GDP-T1 (R=0.89) and GDP-T2 (R=0.87) occurred in the simulation of the daytime (DT) model, and that of the US-Esm (R=0.62) in the nighttime (NT) models. The cross-site simulation was feasible, the best result could up to 0.73 in terms of R. This model provided a quick, efficient, and highly accurate estimation, and could be conducted to estimate the dynamics of CO2 flux where there is no direct in-situ flux measurement. The simulation capability is helpful to characterize the spatial/temporal variations in low-latitude wetland ecosystems, and improve the quantification of global C budget.