摘要
水体预测分析在掌握江河水体的现状、理解污染物质转移的特点以及了解污染源的排污状况进而预测水体发展趋势等方面有着重要意义。有效的水资源管理和明确的水污染治理的区域规划是水生态环境保护的首要任务,而水体的预测分析则是基本保障。本文以某河流域为研究对象,根据其水体现状以及检测的统计数据,创建人工神经网络的水体预测模型,对水环境的关键超标准污染物质总氮的含量进行仿真模拟预测分析。分析数据表明,经过训练后的神经元网络的预测分析偏差低于5%,因而该实体模型能合理地预测分析水体中的总氮浓度值。
Water quality prediction is to predict water quality based on the status quo of river water quality,the characteristics of pollutant migration and the discharge of pollutants.The trend of future changes has a positive significance for the protection of water resources.Reasonable water resources management and water pollution prevention and control Environmental planning is an important task of water environmental protection,and the prediction of water quality can effectively improve the completion of these tasks for protection.Taking Zhuyi River as the research object,combined with its water quality status and monitoring data,a water quality prediction model based on a neural network was established to simulate and predict the concentration of total nitrogen,the main pollutant exceeding the standard in water quality.The results show that the prediction error of the trained neural network is less than 5%,so the model can predict the total nitrogen concentration in water quality well.
出处
《自动化博览》
2022年第12期66-69,共4页
Automation Panorama1
关键词
神经网络
水质
预测模型
Neural network
Water quality
Prediction model
