Sea Level Prediction in the Bay of Bengal — A Case Study

In this scientifically advancing world, it is always forgotten that there is a rising threat to the world: the constantly deteriorating climate and its drastic impact on the well-being of people throughout the globe. The next five years is the best window to take drastic, large-scale measures to stop climate change before it’s too late. The constant and accurate monitoring of the weather parameters is one of the crucial steps in solving climate change. Monitoring and predicting the sea level parameters is one of them. Extreme Sea Level (ESL) have risen significantly during the past few years due to the increase in temperature because of greenhouse gases also slowly but steadily, the glaciers in the north poles are being dissolved. This paper revolves around using different Machine Learning Algorithms to predict and classify the sea level in a categorical manner. The results show that Decision Tree (DT) using Random Forest (RF) and Gradient boost performs the best for the prediction process where the accuracy, precision, recall, and $\mathbf{F 1}$ scores were $\mathbf{9 9. 5 8 \%}, \mathbf{9 9. 5 9 \%}, \mathbf{9 9. 5 8 \%}$, and $\mathbf{9 9. 5 8 \%}$, respectively for the DT suing RF model and the accuracy, precision, recall, and F1 scores were $\mathbf{9 9. 5 8 \%}$, 99.59%, 99.58%, and $\mathbf{9 9. 5 8 \%}$, respectively for the Gradient Boost (GB) model.