Analyzing the effectiveness of MEMS sensor and IoT in predicting wave height using machine learning models

Abstract

Abstract Wave height is a critical consideration in the planning and execution of maritime projects. Wave height forecasting methods include numerical and machine learning (ML) techniques. The traditional process involves using numerical wave prediction models, which are very successful but are highly complex as they require adequate information on nonlinear wind–wave and wave–wave interactions, such as the wave energy-balance equation. In contrast, ML techniques can predict wave height without prior knowledge of the above-mentioned complex interactions. This research aims to predict wave height using micro-electromechanical systems (MEMS), internet of things (IoTs), and ML-based approaches. A floating buoy is developed using a MEMS inertial measurement unit and an IoT microcontroller. An experiment is conducted in which the developed buoy is subjected to different wave heights in real time. The changes in three-axis acceleration and three-axis gyroscope signals are acquired by a computer via IoT. These signals are analyzed using ML-based classification models to accurately predict wave height. The obtained validation accuracy of the ML models K-NN (K-nearest neighbor), support vector machine, and the bagged tree is 0.9906, 0.9368, and 0.9887 respectively, which indicates that MEMS and IoT can be used to accurately classify and predict wave heights in real-time.