Search

In this work, we seek to highlight the impact of wind speed on the fishing effort and, therefore, on the catch, profit, and also the biomass of the caught species. This is to show that the wind factor is a non-negligible element during the fishing. To this end, we consider a bioeconomic model for Sardina pilchardus, the most fished marine species in Morocco, along with a probabilistic model to capture the meteorological changes in the south of Morocco, one of the windiest coastal regions in the country. These two models allow us to calculate the optimal daily fishing effort during a whole year under meteorological constraints, and enable compliance with the respect of the sustainability of the stock of the studied marine species. We can then deduce the catch, the profit, and the state of the biomass from the calculated effort. We then compare these results with those of a deterministic model to highlight the effect of meteorological factor on fishing.

The use of conditional probabilities has gained in popularity in various fields such as medicine, finance, and imaging processing. This has occurred especially with the availability of large datasets that allow us to extract the full potential of the available estimation algorithms. Nevertheless, such a large volume of data is often accompanied by a significant need for computational capacity as well as a consequent compilation time. In this article, we propose a low-cost estimation method: we first demonstrate analytically the convergence of our method to the desired probability and then we perform a simulation to support our point.