Predicting Sea Level Rise in Coral Sea Using Machine Learning

This research investigates the use of machine learning techniques, specifically Multiple Linear Regression (MLR) and Adaptive Neuro-Fuzzy Inference System (ANFIS), to predict sea level rise in the Coral Sea region. Climate change has led to a continuous rise in sea levels, posing significant risks, particularly in coastal areas. Traditional prediction methods often fail to capture the complex dynamics of sea level fluctuations. By leveraging historical sea level data from the Pacific Sea Level and Geodetic Monitoring Project, this study aims to improve prediction accuracy using MLR and ANFIS models. The dataset spans from 1995 to 2021 and includes variables such as sea level, water temperature, air temperature, and wind speed. Our findings indicate that while both models can predict sea level changes, the ANFIS model outperforms MLR in terms of accuracy, as evidenced by lower Mean Squared Error (MSE) and Mean Absolute Error (MAE) scores. However, the predictive power of these models is limited by data quality and model parameter tuning. Further research should focus on improving data preprocessing and optimizing model parameters to enhance prediction reliability. This study contributes to the development of robust sea level prediction frameworks, supporting effective coastal management and climate adaptation strategies.