Analyzing the pollution in rivers and lakes by using artificial neural Article

Analyzing the pollution in rivers and lakes by using artificial neural network, fuzzy logic, and regression methods - Article Example The basic approach is to train an ANN unit using a set of already known set of data to predict a similar future event. In this study feed forward ANN could be used to relate the DO measured at each location to predict the other quality parameters as reported. In this method, the (DO)i is considered as the input parameter to the input-neurons which would be passed into the hidden layer of neuron set after multiplying with a correction weight (kj). The main role of the hidden neuron is to add up the weighted sum received from the input set and combines it with a bias (bj) to determine a net value ( netj = ïÆ' ¥ (DO)i kj - bj ). This net value is passed into the output neuron which uses a non-linear function , say, f(net) = 1/ (1+e-net) to determine the output parameters BOD, NO3, NO2 and PO4 (Tayfur and Singh, 2006). The fuzzy logic approach for the prediction of dissolved oxygen level is based on the set of rules that is in built in the model. This process is undertaken in four different steps. Allocation of partial belonging to each input variable in the form of membership function , which takes up values from 0 to 1, is the first step. This process is referred as fuzzification. The allocation is based on intuition and linear triangular functions are the commonly adopted one. The fuzzy rule base is the next requirement to relate the input and the outputs using if-then logical relations. In the present work it could be either as - If BOD is low , NO3 is low and PO4 is low then DO is high - or If BOD is high, NO3 is high and PO4 is high then DO is low. Next is the fuzzy output subset construction by addition of all the fuzzy subsets. The fuzzy output function need to be converted to discrete form of results using defuzzifcation methods. Centre of gravity method (COG method) could be used to this process for the present situation (Tayfur and Singh, 2006, Chen et