Machine-Learning-Based Sensor Design for Water Salinity Prediction: A Conceptual Approach

Bachar Mourched (), Mariam Abdallah, Mario Hoxha and Sabahudin Vrtagic
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Bachar Mourched: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
Mariam Abdallah: Faculty of Science III, Lebanese University, Tripoli 90656, Lebanon
Mario Hoxha: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
Sabahudin Vrtagic: College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait

Sustainability, 2023, vol. 15, issue 14, 1-12

Abstract: This research paper introduces a sensor that utilizes a machine-learning model to predict water salinity. The sensor’s concept and design are established through a simulation software which enables accurate modeling and analysis. Operating on the principle of light transmission physics, the sensor employs data collected from the simulation software as input parameters to predict the salinity parameter, serving as the output. The results of the prediction model exhibit excellent performance, showcasing high accuracy with a coefficient of determination value of 0.999 and a mean absolute error of 0.074. These outcomes demonstrate the model’s ability, particularly the multi-layer perceptron model, to effectively predict salinity values for previously unseen input data. This performance underscores the model’s accuracy and its proficiency in handling unfamiliar input data, emphasizing its significance in practical applications.

Keywords: COMSOL; light transmission; machine learning; multi-layer perceptron; water salinity (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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