Predictive Analytics for Environmental and Oceanographic Systems

Abstract

Fishing zone prediction is a deal because it helps fishers make a living and plan their trips better. Old ways of finding fishing spots use fixed rules, but they do not work well because ocean conditions keep changing. These changes affect where fish gather. This paper talks about a way to predict where fish will be. It uses data to forecast and map high-density fishing zones every week. The study checks two methods: the baseline ARIMA model and the Sequential Environmental Gradient Network (SEGN) model. The new workflow has four steps: Cleaning Data, Training Models, Predicting fishing zones, and Checking Errors. The results show SEGN works better; ARIMA is still useful for simple predictions. The study also finds that finding the balance between being precise and catching all the fish is tricky. Next steps include using ocean data, improving predictions, and helping fishers make better decisions. The goal is to support fishers and make their operations better. The study aims to make fishing more efficient and effective. It wants to help fishers catch fish while being sustainable. Fishing zone prediction can make a difference in their lives.