RETRACTED ARTICLE: Maritime tropical cyclone based on machine learning and marketing strategy in coastal areas

Abstract

Tropical cyclone is one of the major meteorological disasters affecting China. It is very important for China’s economic development and national defense construction to improve the level of tropical cyclone research and prediction. In recent years, the objective prediction level of tropical cyclone has been greatly improved, but the improvement of intensity prediction is relatively small. The uncertainty of long-term change prediction such as generation frequency is still great, which is still the focus of scholars. In a complex environment, it is very challenging to fully understand the potential characteristics of the transaction, so as to design the best mechanism to effectively obtain benefits from the transaction. In this paper, the random forest algorithm is used to study the intensity prediction of tropical cyclone with the same data, so as to investigate the applicability of the machine learning method and try to improve the numerical prediction results. It is found that the machine learning method of random forest has better prediction ability for tropical cyclone intensity, which is better than GEFs results. With the continuous improvement of the level of socialist modernization, the relationship between the construction quality of market economy and marketing strategy has become increasingly close. But because of the tropical cyclone near the sea area, the economic problems of coastal areas are caused. Based on the current research situation, this paper first introduces the role and influence of the new economic background on the marketing strategy, then discusses the problems that often appear in the process of marketing strategy development planning in coastal areas at present, and, combined with specific problems, puts forward the corresponding optimization solutions, hoping to further promote the application of marketing strategy in coastal areas and to provide necessary technical support for the sustainable and healthy development of enterprises in coastal areas.