摘要
近年来,随着人工智能应用范围的逐渐扩大,各行各业都与人工智能存在或多或少的联系。传统的水质监测方法包括人工采样与实验室分析、现场检测和遥感技术等,这些方法存在时效性差、覆盖范围有限、数据不连续且成本高昂等问题。神经网络的出现大幅提升了传统技术在预测和数据处理方面的效果。在此基础上,通过粒子群算法对BP神经网络进行优化(PSO-BP),结果显示优化后的模型具有更高的准确度和更小的误差。这不仅进一步提高了水质监测的准确性和时效性,还显著降低了监测成本,节省了人力、物力和财力,为水质监测提供了一种新的技术手段。
In recent years,with the gradual expansion of the scope of AI applications,various industries are connected with Artificial Intelligence more or less.Traditional water quality monitoring methods include manual sampling and laboratory analysis,on-site testing,and remote sensing technology,which exist poor timeliness,limited coverage,discontinuous data,and high costs and other problems.The advent of neural networks has significantly enhanced the effect of traditional technologies in prediction and data processing.Based on this,the BP neural network is optimized using Particle Swarm Optimization.The results show that the optimized model achieves higher accuracy and smaller error.This not only further improves the accuracy and timeliness of water quality monitoring but also significantly reduces monitoring costs,saving manpower,material resources,and financial resources.It provides a new technical means for water quality monitoring.
作者
闫佳
刘倩男
刘诚
YAN Jia;LIU Qiannan;LIU Cheng(School of Computer Science,Xi'an Shiyou University,Xi'an 710065,China)
出处
《现代信息科技》
2025年第3期153-156,163,共5页
Modern Information Technology
关键词
人工智能
水质监测
粒子群算法
BP神经网络
Artificial Intelligence
water quality monitoring
Particle Swarm Optimization
BP neural network
