Chang Fi-John, Tsai Yu-Hsuan, Chen Pin-An, Coynel Alexandra, Vachaud Georges
Department of Bioenvironmental Systems Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan, ROC.
Department of Bioenvironmental Systems Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan, ROC.
J Environ Manage. 2015 Mar 15;151:87-96. doi: 10.1016/j.jenvman.2014.12.014. Epub 2014 Dec 24.
Contrasting seasonal variations occur in river flow and water quality as a result of short duration, severe intensity storms and typhoons in Taiwan. Sudden changes in river flow caused by impending extreme events may impose serious degradation on river water quality and fateful impacts on ecosystems. Water quality is measured in a monthly/quarterly scale, and therefore an estimation of water quality in a daily scale would be of good help for timely river pollution management. This study proposes a systematic analysis scheme (SAS) to assess the spatio-temporal interrelation of water quality in an urban river and construct water quality estimation models using two static and one dynamic artificial neural networks (ANNs) coupled with the Gamma test (GT) based on water quality, hydrological and economic data. The Dahan River basin in Taiwan is the study area. Ammonia nitrogen (NH3-N) is considered as the representative parameter, a correlative indicator in judging the contamination level over the study. Key factors the most closely related to the representative parameter (NH3-N) are extracted by the Gamma test for modeling NH3-N concentration, and as a result, four hydrological factors (discharge, days w/o discharge, water temperature and rainfall) are identified as model inputs. The modeling results demonstrate that the nonlinear autoregressive with exogenous input (NARX) network furnished with recurrent connections can accurately estimate NH3-N concentration with a very high coefficient of efficiency value (0.926) and a low RMSE value (0.386 mg/l). Besides, the NARX network can suitably catch peak values that mainly occur in dry periods (September-April in the study area), which is particularly important to water pollution treatment. The proposed SAS suggests a promising approach to reliably modeling the spatio-temporal NH3-N concentration based solely on hydrological data, without using water quality sampling data. It is worth noticing that such estimation can be made in a much shorter time interval of interest (span from a monthly scale to a daily scale) because hydrological data are long-term collected in a daily scale. The proposed SAS favorably makes NH3-N concentration estimation much easier (with only hydrological field sampling) and more efficient (in shorter time intervals), which can substantially help river managers interpret and estimate water quality responses to natural and/or manmade pollution in a more effective and timely way for river pollution management.
由于台湾地区短历时、高强度的暴雨和台风，河流流量和水质呈现出截然不同的季节性变化。极端事件即将发生时，河流流量的突然变化可能会严重恶化河流水质，并对生态系统产生致命影响。水质是按月/季度尺度进行测量的，因此，每日尺度的水质估计将有助于及时进行河流污染管理。本研究提出了一种系统分析方案（SAS），以评估城市河流中水质的时空相互关系，并基于水质、水文和经济数据，使用两个静态和一个动态人工神经网络（ANN）与伽马检验（GT）相结合构建水质估计模型。台湾的大汉河流域为研究区域。氨氮（NH3-N）被视为代表性参数，是判断研究区域污染水平的相关指标。通过伽马检验提取与代表性参数（NH3-N）最密切相关的关键因素，用于模拟NH3-N浓度，结果确定了四个水文因素（流量、无流量天数、水温、降雨量）作为模型输入。建模结果表明，配备递归连接的非线性自回归外生输入（NARX）网络能够以非常高的效率系数值（0.926）和低均方根误差值（0.386mg/l）准确估计NH3-N浓度。此外，NARX网络能够较好地捕捉主要发生在枯水期（研究区域为9月至次年4月）的峰值，这对水污染处理尤为重要。所提出的SAS提出了一种很有前景的方法，仅基于水文数据就能可靠地模拟时空NH3-N浓度，而无需使用水质采样数据。值得注意的是，由于水文数据是按日尺度长期收集的，因此可以在更短的感兴趣时间间隔（从月尺度到日尺度）内进行这种估计。所提出的SAS使NH3-N浓度估计更容易（仅需进行水文现场采样）且更高效（在更短的时间间隔内），这可以极大地帮助河流管理者更有效、及时地解释和估计水质对自然和/或人为污染的响应，以进行河流污染管理。
