Study and Design of a Machine Learning-Enabled Laser-Based Sensor for Pure and Sea Water Determination Using COMSOL Multiphysics  
[PERSON]  
1 College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait; [EMAIL_ADDRESS] (N.F.); [EMAIL_ADDRESS] (B.N.); [EMAIL_ADDRESS] (S.V.)  
[PERSON]  
1 College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait; [EMAIL_ADDRESS] (N.F.); [EMAIL_ADDRESS] (B.N.); [EMAIL_ADDRESS] (S.V.)  
[PERSON]  
2 Faculty of Science III, Lebanese University, Tripoli 90656, Lebanon; [EMAIL_ADDRESS]  
[PERSON]  
1 College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait; [EMAIL_ADDRESS] (N.F.); [EMAIL_ADDRESS] (B.N.); [EMAIL_ADDRESS] (S.V.)  
[PERSON]  
1 College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait; [EMAIL_ADDRESS] (N.F.); [EMAIL_ADDRESS] (B.N.); [EMAIL_ADDRESS] (S.V.)  
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
Footnote 2: [[https://www.mdpi.com/journals/pdf/14](https://www.mdpi.com/journals/pdf/14)]([https://www.mdpi.com/journals/pdf/14](https://www.mdpi.com/journals/pdf/14))  
employed COMSOL to prove the concept of different seawater sensors. However, using a combination of COMSOL Multiphysics and a machine learning approach to develop seawater sensors is not fully explored. In reference [8], hybrid machine learning algorithms are used to predict chlorophyll sensor parameters located in the upper ocean through the sea ice buoy. Nonetheless, another process should be used to convert data to relevant ocean parameters. Reference [9] presents a new methodology for seawater concentration estimation using generalized radial basis function neural networks method. Although all of the existing techniques developed new methods, approaches, and fabrication techniques for seawater sensors, it is still necessary to develop a simple, effective, economical, and high-performance sensors for detecting the nature of the water (pure water or saltwater), as well as its temperature, among other applications.  
In this paper, we present a novel approach to seawater sensors that combines machine learning and optics concepts. This technique is a low-cost technology with label-free biochemical detection, real-time monitoring, and high sensing performance that allows recognition of water type under various temperature conditions. The designed sensor can pave the way for the development of a multi-parameter sensor with a wide range of applications in water pollution and environmental monitoring, among other future applications.  
The paper is organized as follows. Section 1 introduces the scope and importance of the research. Section 2 provides a brief overview of the different methods used for pure and salty water sensing. Several research-based devices are listed. Section 3 elaborates the concept and methodology used to design the proposed sensor. Furthermore, COMSOL Multiphysics simulation results are presented, and the machine learning process and obtained data are provided and analyzed. Section 4 describes the obtained sensor's performance. Section 5 is dedicated for conclusions.