Evaluation of Artificial Neural Networks for Electrical Conductivity and Flow Rate-based Prediction of the Nitrate Nitrogen Concentration in the U-Tapao Canal, Hatyai, Thailand

Abstract

The aim of this study was to identify suitable artificial neural network (ANN) models for the EC-based and flow rate-based prediction of the nitrate nitrogen (NO3-N) concentration in the U-Tapao canal, located in the southern part of Thailand. Two types of four layer ANNs of the feed-forward back propagation (FFBP) and cascade-forward back propagation (CFBP) types were evaluated for this prediction. The selected inputs of the ANNs were EC and flow rate, which were collected daily from December 2014 to March 2015. Overall, the study found that the four layer FFBP with 2 neurons in the input layer, 20 neurons in the first hidden layer, 30 neurons in the second hidden layer, and a single neuron in the output layer with a tan-sigmoid transfer function was the optimal model. The FFBP model produced slightly more accurate results than the CFBP model. Linear regression analysis was used to predict NO3-N, which was compared with the results of the ANNs and the performance of the ANNs was better than that of the linear regression analysis. Therefore, the ANN approach proved to be suitable as an alternative to laboratory-based analysis for the prediction of NO3-N values in the U-Tapao canal.

Keywords

• nitrate nitrogen concentration prediction
• artificial neural network (ANN)
• U-Tapao canal.model