Fishing Model based on Seawater Temperature Prediction

Shaoyou Ao

《International Core Journal of Engineering》 7卷9期 (2021/09) Pp. 249-256

https://doi.org/10.6919/ICJE.202109_7(9).0037

摘要

Seventy percent of the Earth's surface area is covered by water. As the pace of global warming accelerates, some species in the ocean have to make compromises, and we have to be forced to make compromises. Therefore, this article predicts the interaction between us and marine populations. The impact will become very meaningful. First, I have established research indicators to study the effects of changes in ocean water temperature on the habitat of Scottish herring and mackerel. In the light of seasonal time series based on world ocean temperature changes over the past 20 years. I built a seasonal time series ASIMA model of seawater temperature, based on which we made water temperature forecasts around the North Atlantic for the next 50 years, and identified the most likely locations for Scottish herring and mackerel in the next 50 years.Secondly, using prediction data for bilinear interpolation to determine the best or worst impact of Scottish herring and mackerel habitats on North Atlantic small fishing companies, and the time that the impact is most likely to pass.Thirdly, from the above directions, we have established a 0-1 integer programming model that considers the business strategy of small fishing companies to increase revenue through site selection, and recommendations for the strategy when it is used to relocate some fisheries to another country.What's more, in the pros and cons test of the model, the time series model is applied in this case with a high degree of fit, under the same conditions, the fitting degree is much higher than the Grey Model. The comparison between the two shows that the time series model is more suitable in this case. Then analyze the advantages and disadvantages of 0-1 integer planning in the location problem.

關鍵字

ARIMA Model ； Bilinear Interpolation ； 0-1 Integer Programming Model

參考文獻

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