Optimization of water chlorophyll a concentration prediction model based on BP neural network
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Abstract:
Combining automatic monitoring data and neural network method is one of the main methods to predict the chlorophyll a concentration in waterbody.However,the prediction accuracy and stability of the traditional BP neural network model are questionable due to the limitations of the local search with the gradient descent method.To solve this problem,the global search algorithm EMA was used to optimize BP neural network weights and thresholds to improve the chlorophyll a prediction efficiency.The partial derivative method was used to analyze the sensitivity of the input factor in prediction model,and then to simplify the number of input factors.The results showed that EMA could significantly improve the stability and accuracy of network training in the BP neural network prediction model for chlorophyll a concentration.The prediction accuracy ranged from ［0.364,0.978］ to ［0.917,0.983］,and the average prediction accuracy improved from 0.950 to 0.968.The predictive model was more stable using Dimopoulos sensitivity analysis to reduce the model input factor from 12 to 8.The average prediction accuracy decreased from 0.968 to 0.962 and the prediction accuracy ranged from ［0.917,0.983］to ［0.921,0.976］. Under the condition that the number of input factors was 8,the average prediction accuracy with the input factors selected by the sensitivity analysis of Dimopoulos method was significantly higher than that with the input factors based on traditional PCA method.The study results can provide reference for input factor optimization based on BP neural network on chlorophyll a prediction model to improve the stability of model prediction.
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