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Fisheries Catch forecast model

Fisheries Catch forecast model

Model of catch for artisanal fisheries

About

This AI model addresses the critical challenges the Small-Scale fisheries face in the Gulf of California, a pivotal region for Mexico's fisheries production and biodiversity. Amid climate change's backdrop, these tools leverage Deep Learning techniques to forecast fish catch volumes, contributing to sustainable resource management and informed decision-making. The goal is to employ deep learning models to explore the complex dynamics between oceanic conditions and catch volumes, thereby aiding in the sustainable management of fisheries in the Northern Gulf of California. This involves analyzing the intricate relationships and predicting future catch volumes amidst evolving climate patterns.

Builders

3
HM

Hem Nalini Morzaria-Luna

I translate modeling approaches for management and conservation of marine resources. https://hmorzaria.github.io/hemnalinimorzarialuna.quarto.io/

Ricardo Cavieses

Ricardo Cavieses

Marine Science researcher

HM

Hem Nalini Morzaria

Marine ecologist building code to inform climate change research