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人工智能技术在大气环境领域的应用

梁常德 尹民 胡清 游勇 黄芊蕙 许盛彬

梁常德,尹民,胡清,等.人工智能技术在大气环境领域的应用[J].环境工程,2025,43(4):98-109. doi: 10.13205/j.hjgc.202504010
引用本文: 梁常德,尹民,胡清,等.人工智能技术在大气环境领域的应用[J].环境工程,2025,43(4):98-109. doi: 10.13205/j.hjgc.202504010
LIANG C D,YIN M,HU Q,et al.Applications of artificial intelligence technology in the atmospheric environment field[J].Environmental Engineering,2025,43(4):98-109. doi: 10.13205/j.hjgc.202504010
Citation: LIANG C D,YIN M,HU Q,et al.Applications of artificial intelligence technology in the atmospheric environment field[J].Environmental Engineering,2025,43(4):98-109. doi: 10.13205/j.hjgc.202504010

人工智能技术在大气环境领域的应用

doi: 10.13205/j.hjgc.202504010
基金项目: 

国家重点研发计划“区块链边缘计算原型系统设计和应用开发”(2023YFB2704604);深圳市国家智能社会治理实验特色基地(环境治理)项目(SZDL2023001177)

详细信息
    作者简介:

    梁常德(1980-),男,硕士,高级工程师,主要研究方向为生态环境大数据技术与应用。836780343@qq.com

    通讯作者:

    许盛彬(1990-),男,硕士,中级工程师,主要研究方向为生态环境大数据技术与应用。xusb@mail.sustech.edu.cn

Applications of artificial intelligence technology in the atmospheric environment field

  • 摘要: 近年来,人工智能技术迅速发展,引起了各领域研究人员的关注,在农业、气候、安全、环境等不同学科领域均取得了丰硕成果。首先对模糊逻辑、遗传算法、人工神经网络、支持向量机、轻量级梯度提升机等人工智能技术的基本原理、技术特征和适用范围进行了总结,重点阐述混合智能系统的技术先进性和优势;其次,以大气环境领域为例,调研了人工智能技术在环境领域的应用进展,借助具体案例说明了常见的输入参数类型和性能评价指标;最后,从环境要素数据集、模型训练方式、算法自有缺陷3个方面分析了现阶段人工智能技术在大气环境领域存在的挑战,提出了人工智能技术在大气环境领域未来的应用发展趋势主要为混合智能系统组合研究、多环境要素综合分析及其与环保平台的协同应用。
  • 1  大气环境领域的人工智能分类树

    注: GA-ANN为遗传算法与人工神经网络;ES-ANN为专家系统与人工神经网络;ANN-ANN为由2种或以上ANN技术组合的混合人工神经网络系统;PSO-ANN为粒子群优化神经网络; GRU为循环神经网络。

    1.  A classification tree of AI for atmospheric environmental fields

    3  人工智能算法在空气污染预测中的建模流程29

    3.  Modeling process flowchart for AI in air pollution forecasting29

    1  不同人工智能技术简介

    1.   Introduction to different AI technologies

    类型机制优势短板
    MLP各层神经元全连接,无层内连接,无跨层连接非线性映射、有利于并行运算泛化能力差、处理多维数据能力差
    RBFN只有1个隐藏层,具有局部映射的特征具有极快的学习收敛速度,具有最佳逼近性能和全局最优特性解释性差,数据不充分时无法工作,难以确定隐藏层节点数、节点中心和宽度
    RNN隐藏层节点输出取决于当前节点输入和上个节点值,实现层内神经元连接和循环提取时序特征能力强、泛化能力相对较好,适合解决时间依赖性问题[24]输入输出序列不同、处理长期依赖精度下降,存在梯度爆炸和梯度消失问题[25]
    LSTMRNN的改良体,细胞状态,只有少量线性交互在序列建模问题上有一定的优势,具有长时记忆功能并行处理存在劣势,训练时间较长,效率较低,处理小规模数据效果不理想
    CNN神经元之间局部连接,卷积核特征提取[26]稀疏连接、权值共享、计算量小、无需复杂的预处理[26]输入图像尺寸固定
    SVM基于结构化风险最小化原则,用二次元求解支持向量高鲁棒性、全局搜索能力,所需数据量小,过拟合概率小[27]大数据集效率低[28],批量运行时内存及CPU需求量大
    GA模拟了生物进化论中的自然选择和遗传机制具有良好的全局优化能力,与其他模型方法结合较灵活[28]计算量大[28],在达到理想效果前需要多次试错
    FL一种类似于人类推理的推理方法,涉及YES和NO之间的所有中间可能性鲁棒性强,较强的容错能力,构建任意复杂性的非线性函数的能力[29]精度较低,难定义控制目标[28]
    XGBoost基于预排序方法的决策树算法支持并行计算,准确精度较高[30],可扩展性高[31],可进行缺失值处理内存空间消耗大
    LightGBM弱分类器(决策树)迭代训练,利用网络通信算法来优化并行学习逐叶树木生长式算法,训练时间短,支持高效并行处理,内存消耗少[32]可能发生过拟合
    下载: 导出CSV

    2  不同人工神经网络结构特征

    2.   Structural characteristics of different ANNs

    a-MLPb-RBFNc-CNN
    d-RNNe-LSTM
    下载: 导出CSV

    2  不同混合人工智能系统简介

    2.   Introduction to different AI technologies

    类型机制组合优势文献
    FNN模糊逻辑理论、遗传算法、神经网络之间的协同协作扩大处理范围(可处理定量和定性信息),提高信息处理速度[65]
    ES-ANN融合ANN与ES系统响应速度快,抗干扰、知识自动化获取能力及解释能力较强[6667]
    ANFIS基于ANN和FL的数据驱动建模技术,输入与输出映射通过基于网络形式的模糊规则连接灵活性、处理速度和适应能力得到优化[68-71]
    PSO-ANN包括内外两层,外层为PSO算法,内层为ANN网络,实现协同搜索计算克服了局部最小值问题[72]
    GA-ANN将GA与神经网络结合以迭代优化神经网络参数网络配置得到优化,预测效率、灵活性及稳定性较高[73]
    下载: 导出CSV

    3  人工智能技术在大气环境领域的应用

    3.   Applications of AI technology in the atmospheric environment field

    年份类型研究对象输入参数输出参数性能评价指标文献
    2009GA空气污染预测PM10样本数据PM10RMSE[89]
    2012MLP、RBFN、GRNN城市空气质量气温、相对湿度、风速、空气质量监测数据可吸入颗粒物、SO2、NO2RMSE、MAE[90]
    2016ANFIS空气质量预测气压、温度、湿度、能见度、露点、风速、降水等SO2、NO2、CO、O3、PM10R2、NMRS等[91]
    2016MLP空气中CO浓度温度、风速、湿度、CO、水分CO浓度MAE、R2[92]
    2018ARIMAX-MLPPM2.5预测温度范围、湿度范围、日照时长、风速、风向、NO2、SO2、O3、CO、PM2.5PM2.5RMSE、MAE、MAPE[93]
    2019RNN、RF空气污染物预测气象学变量(温度、温度露点差、相对湿度、降水、风速、风向、大气压力和城市热岛)、大气污染物污染物浓度R、RSEM[94]
    2020CNN预测马来西亚巴生市空气污染指数PM10、CO、O3、NO2、SO2APIMAE、RMSE、MAPE、R2[86]
    2020MLPO3预测温度、湿度、风速、风向、O3O3浓度R2、MAE、RMSE[95]
    2020CNN-LSTMPM2.5短期预测气温、风速、风向、气压、累计降雨量等气象因素PM2.5RMSE、MAE、MAPE[96]
    2021MLP、LSTM空气质量评估和污染预测PM10、温度、相对湿度、风速、风向未来某段时间空气质量MAE、MSE、MAPE[97]
    2021LSTM大气不同污染物相关性分析O3、温度、NOx、光合有效辐射等异戊二烯和PM10预测[98]
    2022FL城市空气质量决策支持PM10污染水平和大气稳定性信息不同领域空气质量评价[99]
    2024基于应用策略的LSTM(ASLSTM)PM2.5短期预测PM2.5、O3、PM10、CO、SO2、NO、相对湿度、风速、风向、气温污染物浓度RMSE、MAE[79]
    下载: 导出CSV
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  • 收稿日期:  2024-06-27
  • 录用日期:  2024-10-22
  • 修回日期:  2024-08-22
  • 刊出日期:  2025-04-01

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