TIU Transactions on Inteligent Computing

Forecasting sea level rise using machine learning techniques

Md. Riftabin Kabir, Nazmus Sakib Borson, Sifat Momen & Md. Sazzad Hossain
Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh


During the past few decades, climate change has been posing as a vital game changer for the world stability of natural conditions. The effect can be easily demonstrated via the rise of sea levels on global and local scenarios. Increase of temperature, change in precipitation, melting of glaciers are causing the sea levels to rise in an alarming rate like never before. This particular paper focuses on predicting the sea level of Bangladesh, a third world South Asian regional country using advanced machine learning techniques to produce a potential model for future cautions. The proposed methodology uses climate data of previous 40 years (approx.) from 1977 to 2017 to train our model using different machine learning algorithms like Random Forest (RF), KNN and MLP. In testing phase, KNN algorithm prompted 91.3204% accuracy.

Keywords: Classification, Data Mining, Machine Learning, Sea Level, Prediction, Climate Change