Design of a Portable Water Pollutants Detector Exploiting ML Techniques Suitable for IoT Devices Integration

Analyses of the presence of heavy metals are particularly important in assessing water quality. The monitoring of environmental parameters leads to the collection and analysis of a large set of data that may be used to reduce polluting actions, suggesting that Internet of Things (IoT) technology may provide an efficient contribution to the mitigation of environmental issues. The objective of this paper is to choose the best Machine Learning (ML) model, which can be loaded into a low-power microcontroller, using the sensed data obtained through the Electrochemical Impedance Spectroscopy (EIS) technique as a dataset. The real and imaginary parts of the impedance for each frequency value occurring within the range of 0.18 and 1 Hz are used as features. Five different lead concentrations are used as outputs. The features and the output constitute the dataset. Sixteen distinct scenarios were examined to train the different models in order to investigate ways to reduce the number of features. With the aim of creating a low-energy device that can conduct measurements and predict outcomes locally, the emphasis is on training a variety of models, including qualitative (classification) and quantitative (regression) approaches.