基于SSA优化BP神经网络的污染物浓度二次预测模型

Secondary Prediction Model of Pollutant Concentration Based on SSA Optimized BP Neural Network

为解决一次预报模型模拟结果不理想的情况,使用主成分分析(PCA)对14项气象影响因素进行降维处理并提取4项综合评价指标,使用麻雀搜索算法(SSA)优化BP神经网络的二次预测模型和将一次预报的结果与真实数据的差值作为输出,对预测模型进行训练并做出预测的方法。将模型应用于国内某个地区,用相应的数据对模型进行验证。结果表明,基于SSA优化BP神经网络的预测模型和将误差引入的新模型均较BP神经网络模型有更高的精确度和更强的泛化能力。

In order to solve the problem that the simulation results of the primary prediction model are not ideal,the principal component analysis(PCA)is used to reduce the dimensions of 14 meteorological factors and extract four comprehensive evaluation indicators.The sparrow search algorithm(SSA)is used to optimize the secondary prediction model of BP neural network and the difference between the results of the primary prediction and the real data is used as the output to train the prediction model and make a prediction method.Apply the model to some regions,and verify the model with corresponding data.The results show that the prediction model based on SSA optimized BP neural network and the new model introduced error have higher accuracy and stronger generalization ability than BP neural network model.