Comparative Study of Forecasting Global Mean Sea Level Rising using Machine Learning

Over the last few decades, climate change has become a crucial challenge resulting in the continued burgeoning of the ocean and atmospheric warming, meaning sea levels will likely continue to rise at higher rates than in the present era. Continued sea-level rises may very well lead to cataclysmic natural disasters on a global scale. The current overall local and global sea-level changes are being monitored using tide stations and satellite radar altimeters. However, these tools are not designed to predict a possible future scenario of sea-level rise. The purpose of this paper is to predict the most probable future global sea-level rise using advanced machine learning models. A total of 28 years' worth of sea-level rise data has been utilized for training our models using various machine learning algorithms, e.g., Linear Regression, Moving Average, Dense Neural Network (DNN), WaveNet (A type of Deep Convolutional Neural Network).

Automated summary

Climate change has led to continued ocean and atmospheric warming, causing sea levels to rise, potentially resulting in catastrophic global natural disasters. Current monitoring tools for sea-level changes cannot predict future scenarios, so this study aims to predict future global sea-level rise using advanced machine learning models.