Authors: Lu Wang; Xin Li; Wei Cui
Addresses: Institute of Economics & Management, Yanshan University, Qinhuangdao 066004, China. ' Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China. ' Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China
Abstract: The quality of wetland surface water is a significant factor for the evaluation of the wetland ecological environment. It is also a vital basis for planning and developing wetland tourism. This paper has tried to use Fuzzy Neural Networks (FNN) as a water quality evaluation means, which combines fuzzy membership and neural network frame, to drive the model automatically update in training and leaning processes; thus it can be applied in the simulations of complex systems. The significant factors such as ammonia nitrogen, Dissolved Oxygen (DO), Chemical Oxygen Demand (COD), total nitrogen, total phosphorus and potassium permanganate are selected to indicate the evaluation performance. The experimental results show that the evaluation procedure is consistent with the objective law and the FNN increases the credibility in the assessment of wetland quality.
Keywords: FNN; fuzzy neural networks; fuzzy logic; wetlands; water quality evaluation; water pollution; wetland surface water; ecology; wetland tourism; simulation; modelling; complex systems; wetland quality; environmental quality.
International Journal of Computer Applications in Technology, 2012 Vol.44 No.3, pp.235 - 240
Received: 08 May 2021
Accepted: 12 May 2021
Published online: 12 Sep 2012 *