TITLE:
Forecasting Shark Attack Risk Using AI: A Deep Learning Approach
AUTHORS:
Evan Valenti
KEYWORDS:
deep learning, shark research, predictive ai, marine biology, neural network, machine learning, shark attacks, data science, shark biology, forecasting
JOURNAL NAME:
Journal of Data Analysis and Information Processing,
Vol.11 No.4,
October
12,
2023
ABSTRACT: This
study aimed to develop a predictive model utilizing available data to forecast
the risk of future shark attacks, making this critical information accessible for everyday public use. Employing a deep
learning/neural network methodology, the system was designed to produce a
binary output that is subsequently classified into categories of low,
medium, or high risk. A significant challenge encountered during the study was
the identification and procurement of appropriate historical and forecasted
marine weather data, which is integral to the model’s accuracy. Despite these
challenges, the results of the study were startlingly optimistic, showcasing
the model’s ability to predict with impressive accuracy. In conclusion, the
developed forecasting tool not only offers promise in its immediate application
but also sets a robust precedent for the adoption and adaptation of similar
predictive systems in various analogous use cases in the marine environment and
beyond.