Forecasting Shark Attack Risk Using AI: A Deep Learning Approach

作     者：Evan Valenti Evan Valenti

作者机构：SafeWaters.AI Boston USA 

出 版 物：《Journal of Data Analysis and Information Processing》 (数据分析和信息处理（英文）)

年 卷 期：2023年第11卷第4期

页      面：360-370页

学科分类：0202[经济学-应用经济学] 02[经济学] 

主　　题：deep learning shark research predictive ai marine biology neural network machine learning shark attacks data science shark biology forecasting 

摘      要：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.