Using Logistic Regression in the Early Detection of Tsunamis caused by Earthquakes

Many tsunamis have enough energy to cause a large amount of human death, as well as trillions of dollars in damage repair. The problem arises on how to better prepare for these natural disasters and limit its damage, especially in countries such as Japan that are frequented by tsunamis.

Tsunamis have forced large financial investments devoted to their preparation and repair. One primary cause of such tsunamis are earthquakes, often those occurring nearby. By using processed NCEI/WDS datasets for earthquakes and tsunamis, we built a logistic regression model to predict the probability a tsunami would form following a given earthquake using features such as magnitude and location with an accuracy of around 80%. In addition, a linear regression model was used to predict the resulting tsunami’s size, although it had low efficacy.

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Machine Learning Algorithms for Consumer Plastics Identification and Sorting

Seeking a solution to maintain plastic recycling costs while increasing the output of reusable material, this paper poses the question “what type of machine learning algorithm is most suitable for a plastic identification system in consumers’ homes?” It investigates five total machine learning algorithms to determine which one best balances accuracy, management of resources, and time efficiency, ultimately arriving at the conclusion that a Support Vector Machine that uses a Polynomial Kernel is the best algorithm and serves to demonstrate that algorithms such as the ones analyzed have become advanced enough for more efficient, AI driven systems to replace those of the status quo.

Alexander K.

Mentored by Eric Bradford

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Combating Climate Fake News Using NLP

As fake news becomes more prevalent across the US, important issues become harder to solve. One such issue is climate change, where climate misinformation has worsened viewer’s abilities to distinguish between fake information and real information. This project’s objective is to tackle climate misinformation using an artificial intelligence model.

Rayyan M.

Mentored by Philip Bell

Prediction of Nitrogen Dioxide Level using Machine Learning Models

As we release more pollution into the atmosphere as factory and vehicle byproducts, acid rains are formed and are constantly damaging the environment and harming wildlives under and above the ocean. If the occurrence of acid rains can't be limited, the phenomenon of this man-made disaster will threaten the health and safety of billions. By predicting the potential development of acid rain with nitrogen dioxide (NO2) levels, one of the major components of acid rain, we could prevent it by limiting the number of specific gasses produced.

Audrey W.

Mentored by Matthew Radzihovsky

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Using Logistic Regression in the Early Detection of Tsunamis caused by Earthquakes

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Research Paper