  Water is a necessary fluid to the human body and automatic checking of its
quality and cleanness is an ongoing area of research. One such approach is to
present the liquid to various types of signals and make the amount of signal
attenuation an indication of the liquid category. In this article, we have
utilized the Wi-Fi signal to distinguish clean water from poisoned water via
training different machine learning algorithms. The Wi-Fi access points (WAPs)
signal is acquired via equivalent smartphone-embedded Wi-Fi chipsets, and then
Channel-State-Information CSI measures are extracted and converted into feature
vectors to be used as input for machine learning classification algorithms. The
measured amplitude and phase of the CSI data are selected as input features
into four classifiers k-NN, SVM, LSTM, and Ensemble. The experimental results
show that the model is adequate to differentiate poison water from clean water
with a classification accuracy of 89% when LSTM is applied, while 92%
classification accuracy is achieved when the AdaBoost-Ensemble classifier is
applied.
