Water Level Estimation by Means of Microwave Reflection Measurements and Machine Learning Processing in a Multimode Cavity

This paper presents a novel method based on microwave reflection measurements and machine-learning techniques to estimate the water level in a multimode microwave applicator. Accurate water level monitoring is essential to maximize heating efficiency and protect the microwave source from excessive reflected power. To supplement conventional physical sensors, four regression models were evaluated: a one-dimensional convolutional neural network (CNN-1D), a multilayer perceptron (MLP), a support vector regressor (SVR), and a random forest (RF) model. These models estimate the water level based on the reflection coefficient S11 measured under low-power conditions over the 2.2–2.8 GHz band using a waveguide-based measurement setup for embedded sensing. The models were trained and evaluated using either the magnitude, phase, or both of S11 over the full-band or two selected reduced sub-bands. Results demonstrated that several models can accurately estimate water level from low-power microwave measurements, especially when magnitude is used as input data. The results demonstrate the potential of microwave measurements combined with machine-learning as a non-invasive supplementary sensing approach for water level monitoring.