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With the continuous development of industry, the discharge of pollutants in marine areas has attracted widespread attention. As a rapidly developing city, Hong Kong faces significant challenges regarding the large-scale discharge of pollutants into its marine areas. Therefore, studying the inversion model of chlorophyll-a concentration in the coastal waters of Hong Kong is an important topic. Traditional BP neural networks are widely used for their ability. However, it is challenging to scientifically and effectively determine the parameters, and the stability of the model is insufficient. In this study, based on the measured data of chlorophyll-a concentration in the coastal waters of Hong Kong and Landsat 8 OLI data of the Hong Kong offshore area, the traditional BP neural network is optimized and improved. Three neural network models are established: BP neural network model, BP neural network optimized by genetic algorithm (GA-BP), and BP neural network model improved by whale optimization algorithm (WOA-BP). By comparing the prediction accuracy , the results show that the WOA-BP neural network model achieves high accuracy and good stability in the inversion of chlorophyll-a concentration, with an average relative error of 12.91%, which is lower than that of the traditional BP neural network model.
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