Using Random Forest for Future Sea Level Prediction

Abstract

This research paper presents an investigation into using the random forest algorithm for predicting future sea level. Sea level is a critical indicator of the health of our oceans and coastal areas and is measured in total weight observations. The study employs the random forest algorithm, a powerful machine learning technique, to analyze a dataset of sea level observations. The results of the analysis demonstrate the effectiveness of the random forest algorithm in accurately predicting future sea level changes. The findings of this research have important implications for coastal management and adaptation strategies. This research provides a valuable tool for decision-makers and coastal managers, allowing for more informed and proactive planning for sea level rise. Overall, the paper shows that the random forest algorithm is a promising method for sea level prediction and highlights the importance of continued research in this area.