PREDICTION OF SEA LEVEL RISE USING MACHINE LEARNING ALGORITHMS

Abstract

Due to the impact it has on the planet and human lives, global warming is a topic that has become highly significant in the last few decades. Rising sea levels represent one of the most serious consequences of global warming. The main objective of this study is its prediction. First, relevant data was collected from various sources and preprocessed. Multiple models were trained on the preprocessed training set - Support Vector Machine, Naive Bayes model, Random Forest, Bagging, XGBoost, and time series models. Each model outputs the probability of a sea level rise compared to the previous month, while the time series predicted the exact values of the average global sea level occurring. The conclusion is that the primary influence on the sea level rise is the emission of harmful gases (i.e., carbon dioxide) and temperature on the water surface and land.