https://scholars.lib.ntu.edu.tw/handle/123456789/380536
標題: | Regional estimation of groundwater arsenic concentrations through systematical dynamic-neural modeling | 作者: | Chang, F.-J. Chen, P.-A. CHEN-WUING LIU VIVIAN LIAO Liao, C.-M. |
關鍵字: | Arsenic (As); Bayesian regularization; Gamma test; Groundwater quality; Indicator kriging; NARX network | 公開日期: | 2013 | 卷: | 499 | 起(迄)頁: | 265-274 | 來源出版物: | Journal of Hydrology | 摘要: | Arsenic (As) is an odorless semi-metal that occurs naturally in rock and soil, and As contamination in groundwater resources has become a serious threat to human health. Thus, assessing the spatial and temporal variability of As concentration is highly desirable, particularly in heavily As-contaminated areas. However, various difficulties may be encountered in the regional estimation of As concentration such as cost-intensive field monitoring, scarcity of field data, identification of important factors affecting As, over-fitting or poor estimation accuracy. This study develops a novel systematical dynamic-neural modeling (SDM) for effectively estimating regional As-contaminated water quality by using easily-measured water quality variables. To tackle the difficulties commonly encountered in regional estimation, the SDM comprises of a neural network and four statistical techniques: the Nonlinear Autoregressive with eXogenous input (NARX) network, Gamma test, cross-validation, Bayesian regularization method and indicator kriging (IK). For practical application, this study investigated a heavily As-contaminated area in Taiwan. The backpropagation neural network (BPNN) is adopted for comparison purpose. The results demonstrate that the NARX network (Root mean square error (RMSE): 95.11μgl-1 for training; 106.13μgl-1 for validation) outperforms the BPNN (RMSE: 121.54μgl-1 for training; 143.37μgl-1 for validation). The constructed SDM can provide reliable estimation (R20.89) of As concentration at ungauged sites based merely on three easily-measured water quality variables (Alk, Ca2+ and pH). In addition, risk maps under the threshold of the WHO drinking water standard (10μgl-1) are derived by the IK to visually display the spatial and temporal variation of the As concentration in the whole study area at different time spans. The proposed SDM can be practically applied with satisfaction to the regional estimation in study areas of interest and the estimation of missing, hazardous or costly data to facilitate water resources management. ? 2013 Elsevier B.V. |
URI: | http://www.scopus.com/inward/record.url?eid=2-s2.0-84881097497&partnerID=MN8TOARS http://scholars.lib.ntu.edu.tw/handle/123456789/380536 |
DOI: | 10.1016/j.jhydrol.2013.07.008 | SDG/關鍵字: | Bayesian regularization; Gamma test; Ground-water qualities; Indicator kriging; NARX network; Arsenic; Estimation; Groundwater; Health risks; Interpolation; Maps; Mean square error; Neural networks; Space division multiple access; Water pollution; Water quality; Water resources; Water supply; Information management; arsenic; artificial neural network; Bayesian analysis; concentration (composition); drinking water; estimation method; groundwater pollution; groundwater resource; kriging; numerical model; public health; resource management; spatial variation; temporal variation; water quality; World Health Organization; Taiwan |
顯示於: | 生物環境系統工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。