Fuzzy and Neural Network Models for Pollution Concentration Predictions in Streams

The use of herbicides, pesticides, and other chemicals in agricultural fields increases the concentration of chemicals in streams which severely affects the health of human and environment. The transport of chemical pollutants into river or streams is not straight forward but complex function of applied chemicals and land use patterns in a given river or stream basin. The factors responsible for transport of chemicals may be considered as inputs and chemical concentration measurements in streams as outputs. Each of these inputs and outputs may contain measurement errors. Present work uses characteristics of fuzzy sets to address uncertainties in inputs by incorporating overlapping membership functions for each of inputs even for limited data availability situations. The fuzzy c-means (FCM) algorithm is used for the optimization of membership functions of fuzzy rule based models for the estimation of diffuse pollution concentration in streams. The general methodology for estimate of diffuse pollution in streams is presented. The application of the proposed methodology is illustrated with real data to estimate the diffuse pollution concentration in a stream system due to application of a typical herbicide, atrazine, in corn fields with limited data availability. The limited performance evaluation of the methodology was found to work better than artificial neural network, and traditional regression models. Solution results establish that developed fuzzy rule base model with FCM is potentially capable for the estimation of diffuse pollution concentration values in water matrices with sparse data situations.