Sea level anomaly and earthquake predictions: endangered countries prognostications - Hassan, Raini; Saif Utsha, Abid Ebna; Noman, Mahfuzealahi (2020)
Natural calamities are often unforeseen and cause massive destruction. It is extremely difficult to predict natural disasters. Existing machine learning techniques are not reliable enough to find the affected countries due to earthquakes and rising sea levels. This paper aims to use predictive analysis to find the countries that will be affected by earthquakes and rising sea levels. Also, the purpose is to see how machine learning techniques perform in terms of sudden calamities like earthquakes or slow calamities like rising sea level. The results were deduced by data analysis, and deep learning techniques like Long-Short Term Memory (LSTM). It was found out that using the approached method in this paper can accurately identify the countries that are going to be affected and predict both earthquake and sea level anomalies accurately. For earthquake, the model was able to capture the happening of earthquake events into a certain quarter of the year with the Root Mean Square Error (RMSE) of 0.504. And for sea level rise, the RMSE was 0.064. It was concluded that Deep learning techniques (e.g.-LSTM) work well with slow changes like sea level anomaly rather than sudden calamities like earthquakes. The techniques used in this paper can be upgraded further in the future to find and help more endangered countries to be prepared better against these natural calamities.