AUTHOR=Wang Bin 

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

JOURNAL=Frontiers in Ecology and Evolution

VOLUME=Volume 10 - 2022

YEAR=2022

URL=https://www.frontiersin.org/journals/ecology-and-evolution/articles/10.3389/fevo.2022.1043976

DOI=10.3389/fevo.2022.1043976

ISSN=2296-701X

ABSTRACT=Marine ecosystem provides environment, resources and services for the development of human society. In recent years, China's coastal zone has been polluted in varying degrees, which has seriously affected the development of coastal zone. The characteristic of Marine environmental data, the variety of data types, the complexity of factors affecting Marine environment and the unpredictability of Marine pollution. At present, there are few researches on the application of clustering analysis algorithm to Marine environmental monitoring. Then, the carbon emission of coastal area is predicted by Marine environmental data Therefore, this paper mainly studies the accumulation characteristics of marine environmental data in space and time, and uses Fuzzy C-means (FCM) algorithm to mine the data monitored by marine environment; Meanwhile, the prediction of coastal Carbon emission (CE) has been focused on, and the GM-BP algorithm to predict the CEs of coastal areas, which solves the problem that the traditional Back Propagation Neural Network (BPNN) cannot fully learn the data features, which leads to the decline of accuracy. The experimental results show that the FCM algorithm can divide the marine sample data into corresponding categories to distinguish the polluted and unpolluted samples. The improved neural network model has higher nonlinear fitting degree, and its prediction error is obviously better than that of BP neural network. The main contribution of this paper is to firstly study the characteristics of Marine environmental data accumulation in space and time. The academic contribution of this paper is to substitute the predicted values of the three grey models into the neural network structure simulation, and finally get more accurate predicted values. From a practical point of view, this study is helpful to alleviate the pressure of climate change caused by the increase of carbon emissions in global coastal zones to a certain extent. This study can also provide a new method of environmental governance measurement for marine environmental regulatory authorities.