Application of carbon emission prediction based on a combined neural algorithm in the control of coastal environmental pollution in China

The marine ecosystem provides the environment, resources, and services necessary for the development of every human society. In recent years, China's coastal zone has been polluted to varying degrees, which has seriously affected its development. The characteristics of marine environmental data include the variety of data types, the complexity of factors affecting the marine environment, and the unpredictability of marine pollution. Currently, there are few studies applying the clustering analysis algorithm to marine environmental monitoring. Then, carbon emissions (CEs) from coastal areas are predicted using marine environmental data. Therefore, this paper mainly studies the spatial and temporal accumulation characteristics of marine environmental data and uses the fuzzy c-means (FCM) algorithm to mine the data monitored by the marine environment. Meanwhile, it has been focused on the prediction of coastal CEs, and the grey model-back propagation (GM-BP) algorithm has been developed to predict CEs from coastal areas, which solves the problem that the traditional back propagation neural network (BPNN) cannot fully learn data features, which leads to a decline in accuracy. The experimental results showed that the FCM algorithm can divide the marine sample data into corresponding categories to distinguish polluted and unpolluted samples. The improved neural network model has a higher degree of non-linear fit and lower prediction error than a back propagation (BP) neural network. The main contribution of this paper is to first study the spatial and temporal accumulation characteristics of marine environmental data. The academic contribution of this study is to substitute the predictions of the three gray models (GMs) with the neural network structure simulation to finally obtain more accurate predictions. From a practical point of view, this study is helpful to a certain extent in alleviating the pressure of climate change due to increased CEs in global coastal zones. This study can also provide a new method of measuring environmental governance for marine environmental regulatory authorities.

Automated summary

This study focuses on the spatial and temporal accumulation characteristics of marine environmental data and predicts carbon emissions (CEs) from coastal areas using clustering analysis algorithm and neural network algorithm. The experimental results show that these algorithms can effectively classify and predict marine sample data, providing new methods for marine environmental management.