Prediction of Major Wave Heights from Scatterometry measurement Data via Machine Learning Mode

Scatterometry (SC) is a microwave radar sensor that scans the surface from an aircraft or a satellite and measures the normalizing radar crossing section of the surface; the scatterometer can measure wave height and other pertinent data, although the altimeter is more limited in its scope. Presently, the challenging characteristics in scatterometry measurement data, which include natural energy intensity, wind speed stability, improper disaster management, earth atmosphere, predicting ocean current wave height from the observed challenges in this research developing an improved machine learning (MLSC) assisted scatterometer data allows a network program to learn and adapt to new data without the need for human intervention. It keeps a device's built-in algorithms up to date regardless of changes in the global economy. In machine learning-enabled scatterometry data, supervised learning (SL) has been proposed to reduce the natural energy intensity in significant wave height scatterometry measurement and analyze the atmosphere and wind speed stability by improving remote sensing measurement data gathered by sensors. Reinforcement learning (RL) has been included, which helps to reduce the risk of controlling the improper disaster management to improve the predicting ocean current wave height with the impact of earth's atmosphere changes in the climate, the release of pollutants into the atmosphere condition, which helps to enhance the prediction of significant wave height form scatterometry measurement data. The experimental results show the proposed model improves natural energy intensity, wind speed stability, improper disaster management, earth atmosphere, and predicting current ocean waves in the reinforcement learning method.