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人工智能在水污染治理领域的研究进展

魏潇淑 高红杰 陈远航 常明

魏潇淑,高红杰,陈远航,等.人工智能在水污染治理领域的研究进展[J].环境工程技术学报,xxxx,x(x): x-xx doi: 10.12153/j.issn.1674-991X.20210638
引用本文: 魏潇淑,高红杰,陈远航,等.人工智能在水污染治理领域的研究进展[J].环境工程技术学报,xxxx,x(x): x-xx doi: 10.12153/j.issn.1674-991X.20210638
WEI X S,GAO H J,CHEN Y H,et al.Research progress of artificial intelligence in the field of water pollution control[J].Journal of Environmental Engineering Technology,xxxx,x(x): x-xx doi: 10.12153/j.issn.1674-991X.20210638
Citation: WEI X S,GAO H J,CHEN Y H,et al.Research progress of artificial intelligence in the field of water pollution control[J].Journal of Environmental Engineering Technology,xxxx,x(x): x-xx doi: 10.12153/j.issn.1674-991X.20210638

人工智能在水污染治理领域的研究进展

doi: 10.12153/j.issn.1674-991X.20210638
基金项目: 国家重点研发计划项目(2020YFC1807903);中央级公益性科研院所基本科研业务专项(2019YSKY-019)
详细信息
    作者简介:

    魏潇淑(1985—),女,工程师,博士,主要研究方向为水环境污染与控制,weixiaoshu36@163.com

    通讯作者:

    常明( 1981—),女,高级工程师,博士,主要从事流域水环境研究与治理,changming@craes.org.cn

  • 中图分类号: X522

Research progress of artificial intelligence in the field of water pollution control

  • 摘要:

    人工智能技术具有自学习、自适应和自组织的独特性能,在环境工程领域具有较大的应用潜力,目前已被广泛地应用于水环境污染、大气污染、固废处理、气候变化等环境领域,是环境监控和治理的良好助手。尽管我国各类水体水质已有明显改善,但是在水资源严重短缺的今天,水污染防治工作仍然任重而道远,开发人工智能在水污染治理领域的应用具有重大意义。文章论述了人工智能模型的特点和分类,综述了其在水污染治理领域的研究现状和应用进展,展望了人工智能在水污染治理方面的发展前景,为水污染防治技术的开发及应用提供参考依据。

     

  • 图  1  人工智能技术在水污染控制方面的应用模型分类[2]

    Figure  1.  Application model classification tree of AI technologies for water pollution control[2]

    表  1  人工智能技术在水污染治理领域的特点与比较

    Table  1.   Characteristics and comparison of AI technologies in the field of water pollution control

    人工智能技术优点缺点适用性
    人工神经网络(ANNs) 由大量神经元组成,具有大规模并行、分布式存储和处理、自组织、自适应和学习能力,速度快,计算成本低,具有很强的容错性和鲁
    棒性
    需要大量有代表性的数据,学习时间过长,可移植性较差 在模式识别、智能控制、组合优化、预测等领域得到成功应用,可应用于大气质量评价和预警系统、水处理软测量领域、水质预测预警、地表水污染特征识别、城市生活垃圾建模等[2]
    多层感知器神经网络(MLPNN) 具有并行处理和自学习能力;能以任意精度逼近非线性函数 收敛速度慢;存在过度拟合和局部最小值的风险 在模式识别、函数逼近、风险预测和控制等领域中有广泛的应用,目前已经成为污水污染物去除建模和优化的高效工具[12]
    BP神经网络 BP神经网络是一种按误差逆传播算法训练的多层前馈网络,具有较强的非线性映射能力、自学习和自适应能力,以及较强的泛化能力和容错能力 由于存在局部极小问题,使网络不能以高精度逼近实际系统,可能需要多次学习和调整才能成功;算法学习过程收敛速度慢,需要较长的训练时间;对学习样本依赖性较强;网络结构选择不一 是目前应用最广泛的神经网络模型之一,主要用在函数逼近、模式识别、分类、数据压缩等方面,如水处理过程中的优化与控制等[52]
    径向基函数神经网络(RBFNN) 基函数可以是高斯函数,也可以是小波函数,支持在线和离线训练,逼近精度高,几乎能实现完全逼近;结构简单,训练速度快,可进行大范围地数据融合,可并行、高速地处理数据 需要大量的训练数据;需要大量隐层神经元[2] 被广泛用于函数逼近、时间序列分析、数据分类、模式识别、图像处理、系统建模、自动控制和故障诊断等领域,水环境治理方面主要应用于水质预测和污水污染物去除等[53]
    支持向量机(SVM) 在解决小样本、非线性及高维模式识别中具有优势,算法简单,具有较好的鲁棒性 在批量处理模式下训练时,需要大量内存和CPU时间;解决多分类问题存在困难;对缺失数据敏感,对参数和核函数的选择较敏感 具有更为严密的理论和数学基础,可以分析数据、识别模式,广泛应用于统计分类和回归分析,已成功应用于水处理控制、水环境预警与评估领域[28]
    遗传算法(GA) 搜索能力强,具有良好的全局优化能力;个体选择具有随机性;鲁棒性强;易与其他方法或模型相
    结合
    编程较为复杂;很难处理和优化维数较高的问题;迭代次数多导致计算量大,模型效率较低;对初始种群的选择有一定的依赖性;容易出现过早收敛问题,局部搜索能力差 是一种强大的优化工具,目前可用于优化污水处理工艺条件和污染物去除参数,以降低控制
    成本[51]
    模糊逻辑(FL) 可对任意复杂度的非线性函数进行建模;可容忍不精确的数据;可使用任何给定的系统;鲁棒性强,尤其适用于非线性、时变、滞后系统的控制;有较强的容错能力;可快速有效地处理不精确性和非线性 信息简单的模糊处理将导致系统的控制精度降低和动态品质变差;模糊控制的设计尚缺乏系统性,无法定义控制目标 应用于污水处理工艺参数的优化,另外,在环境质量指标设计方面具有巨大应用潜力[39,54]
    模糊神经网络(FNN) 主要指利用神经网络结构来实现模糊逻辑推理,包含模糊逻辑理论和神经网络,具有较强的自学习能力和自整定功能,人工干预少,精度较高,对专家知识的利用较好;对样本的要求较低 计算时间长;在多变量、复杂控制系统中,很难确定网络的结构和规则点的组合“爆炸”问题 可用于模糊回归、模糊控制器、模糊专家系统、模糊谱系分析、模糊矩阵方程、通用逼近器,适用于先进的控制系统,在污水处理领域得到广泛应用[11]
    自适应神经网络模糊推理系统(ANFIS) 基于数据建模,不需实际辨识模式;可对非线性系统进行辨识;收敛快,误差小,泛化能力强 需要样本多,对训练数据质量依赖性高 在预测、控制、数据挖掘和噪声消除等诸多领域具有强大应用价值[48]
    遗传算法-人工神经网络(GA-ANN) 搜索能力强;可防止局部最小值;快速收敛;精度高;有较好的鲁棒性 计算沉重;无法确定隐藏神经元的数量 可用于环境预测预警系统、优化控制器参数、污染物去除建模与优化等领域[55]
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  • 收稿日期:  2021-11-04
  • 网络出版日期:  2022-04-21

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