环境空气污染监测中工程造价智能化预测方法研究

Research on Intelligent Prediction Method of Project Cost in Ambient Air Pollution Monitoring

由于未进行有效地指标选取,导致环境空气污染监测中工程造价预测结果与实际存在较大误差。提出环境空气污染监测中工程造价智能化预测方法。利用层次分析法计算所有拟定影响指标的权重,利用粒子群算法(PSO)优化神经网络模型初始权值和阈值,构建改进神经网络(PSO-ANN)的预测模型,实现空气污染监测站工程造价智能化预测。结果表明所构建预测模型应用预测结果与实际结果更为接近,二者仅相差1.46万元,证明所研究预测方法的准确性更高,为环境工程建设投资提供了可靠依据。

Due to the lack of effective index selection,there is a big error between the project cost prediction result and the actual situation in environmental air pollution monitoring.An intelligent forecasting method of project cost in ambient air pollution monitoring is proposed.Ahp was used to calculate the weights of all the proposed impact indicators,particle swarm optimization(PSO)was used to optimize the initial weights and thresholds of the neural network model,and an improved neural network(PSO-ANN)prediction model was constructed to realize the intelligent cost prediction of air pollution monitoring station.The results show that the application prediction results of the prediction model are closer to the actual results,and the difference between the two is only 14,600 yuan,which proves that the accuracy of the prediction method is higher,and provides a reliable basis for environmental engineering construction investment.