Risk Assessment for River Water Quality Using Artificial Neural Networks
Water quality risk assessment has gradually become a new study field, along with the increase of serious water environmental pollution problems in recent years. Artificial neural network has the good ability to process non-linear information, especially fits for the problems with multi-index complexity and uncertainty in risk assessment. It is adopted in river water quality risk assessment and forecast in this paper, and eight evaluating indicators (Cd, As, Cr, Hg, Pb, CN, NH3 and volatile phenol) are selected as the network input, risk degrees per year as output layer. Then the neural network water quality assessment model based 8-4-1 is constructed. Then the network model is extended to the whole basin to evaluate and forecast health risk caused by gene toxic substances and non-gene toxic substances, and the map for risk assessment and forecast based on evaluated results is made.
artificial neural networks water quality risk assessment Second songhua river
LIAO Xiaoyu TONG Zhijun YANG Lingbin
College of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China College of Urban and Environmental Sciences, Northeast Normal University, Changchun 130024, China
国际会议
The 1st International Conference on Risk Analysis and Crisis Response(首届风险分析与危机反应国际学术研讨会)
上海
英文
772-777
2007-09-25（万方平台首次上网日期，不代表论文的发表时间）