基于径向基函数神经网络(Radial Basis Function Neural Network,RBFNN)构建了一种环境废水质量预测模型,根据湖南省2021-2022年监测的pH值、化学需氧量(Chemical Oxygen Demand,COD)和氨氮浓度数据,预测了2023年各月废水排放的环境质量.模型采用RBFNN单隐层结构,利用Softmax激活函数和梯度下降优化算法对废水数据进行了建模和预测.实验结果表明,pH值在7.2～7.4之间,氨氮浓度稳定在国家一级排放标准以下,COD浓度虽有波动但总体符合国家标准.与传统模型相比,RBFNN能够更好地捕捉数据中的非线性特征,提高了预测精度,展示了RBFNN在环境科学中的应用潜力,并为废水质量预测提供了有效的技术路径.
Research on Constructing a Heavy Metal Wastewater Prediction Model for Enterprises in Hunan Province Based on Artificial Neural Networks
A prediction model for environmental wastewater quality was constructed based on Radial Basis Func-tion Neural Network(RBFNN).and it predicted the environmental quality of wastewater discharge in each month of 2023 using pH value,Chemical Oxygen Demand(COD),and ammonia nitrogen concentration data monitored in Hu'nan Province from 2021 to 2022.The model adopted RBFNN single hidden layer structure and used Soft-max activation function and gradient descent optimization algorithm to model and predict wastewater data.The ex-perimental results showed that the pH value was between 7.2 and 7.4,and the ammonia nitrogen concentration re-mained stable below the national first level emission standard.Although the COD concentration fluctuated,it over-all met the national standard.Compared with traditional models,RBFNN can better capture nonlinear features in data,improve prediction accuracy,demonstrate the potential application of RBFNN in environmental science and provide an effective technical pathway for predicting wastewater quality.
Radial Basis Function Neural NetworkHeavy metal pollutionPrediction modelWastewater discharge
万方数据
维普
