Nitrate and Sulfate Estimations in Water Sources Using a Planar Electromagnetic Sensor Array and Artificial Neural Network Method

Abstract

The primary advantages of planar electromagnetic sensors can be listed as low cost, convenient, suitable for in situ measurement systems, rapid reaction, and highly durable. In this paper, the outputs of a planar electromagnetic sensors array were observed and analyzed after testing it with different types of water samples at different concentrations. The output parameters were derived to decompose by wavelet transform. The energy and mean features of decomposed signals were extracted and used as inputs for an artificial neural network (ANN) model. The analysis model was targeted to classify the amount of nitrate and sulfate contamination in water. Nitrates and sulfate samples in the form of KNO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> and K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> SO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> , each having different concentrations between 5 and 114 mg dissolved in 1 L of distilled water, were used. Furthermore, the analysis model was tested with seven sets of mixed KNO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sub> and K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> SO <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4</sub> water samples. A three-layer multilayer perceptron is used as a classifier. It is understood from the results that the model can detect the presence of nitrate and sulfate added in distilled water and is capable of distinguishing the concentration level in the presence of other types of contamination with a root mean square error (RMSE) of 0.0132. The validity of the ANN model was verified by removing the ANN model in estimating the water contamination, where the RMSE rose to 0.0977. The system and approach presented in this paper have the potential to be used as a useful low-cost tool for water source monitoring.