基于航空图像中的多种气体,精确的两阶段深度机器学习辅助空气质量估计

IF 2.9 4区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES
Iacovos Ioannou, Prabagarane Nagaradjane, Ala Khalifeh, Vasos Vassiliou, Janardhan M, Kaavya S, Vibish Kashyap B, Andreas Pitsillides
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引用次数: 0

摘要

目前,与工业相关的环境因素对人类健康产生了负面影响。衡量空气污染程度的空气质素指数(AQI)正在恶化,影响我们的日常生活。日益严重的空气污染、对气候变化的担忧、不断发展的技术和环境研究都认为空气质量越来越重要。空气污染管理变得至关重要,在日常生活中监测环境状况和空气质量是必要的。最新的空气质量研究并不关注航空图像或特定的污染物。此外,避免处理某些特定天气因素(如温度和湿度)过高的问题。在拟议的研究中,我们通过将调查分为两个阶段来解决上述问题。因此,本文分两个阶段实现了基于图像的高精度空气质量监测系统。在我们研究的第一阶段,流行的深度机器学习预测算法,包括人工神经网络(ANN),卷积神经网络(CNN), CNN-长短期记忆(CNN- lstm)和MobileNetv2网络,用于分配到不同类别的AQI,目标是利用航空图像进行AQI预测。接下来,第二阶段利用人工神经网络、循环神经网络(RNNs)、LSTM和RNN-B-LSTM流行的深度机器学习技术,开发一个回归预测系统,该系统使用第一阶段最高准确率方法(即人工神经网络方法,准确率为99.14%)的特征和空气质量结果分类进行训练。为了解决过拟合/欠拟合的问题,使用了过采样技术(SMOTE)。该方法考虑了各主要污染气体(即PM2.5、PM10、NOx、NH3、SO2、O3)对整体空气质量指数(AQI)的贡献。气温、湿度等天气因素影响空气质量;我们的考试也会考虑这些因素,从而做出更准确的预测。此外,还进行了比较研究,以确定所提出的预测系统的最佳模型架构。最后,结果表明,第一阶段的图像分类应采用人工神经网络(准确率99.14%),这是一种准确率较高的方法;第二阶段的图像分类应采用RNN-B-LSTM方法,同样具有较高的准确率(准确率98.73%)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

An accurate two-stage deep machine learning aided air quality estimation based on multiple gases from aerial images

An accurate two-stage deep machine learning aided air quality estimation based on multiple gases from aerial images

Currently, industry-related environmental factors negatively affect human health. The Air Quality Index (AQI), a measurement of air pollution, is worsening and affecting our daily life. Increasing air pollution, concerns about climate change, evolving technologies, and environmental research estimate air quality as increasingly important. Air pollution management has become crucial, and environmental monitoring conditions and air quality in daily life are necessary. The latest research on AQI does not focus on aerial images or specific pollutants. Additionally, avoid tackling the issue of having a high level of some specific weather elements, such as temperature and humidity. In the proposed research, we tackle the issues mentioned above by splitting our investigation into two phases. Thus, in this paper, an image-based high-accuracy air quality monitoring system is realized in two stages. In the first stage of our examination, popular deep machine learning prediction algorithms, including Artificial Neural Networks (ANN), Convolution Neural Networks (CNN), CNN-Long Short Term Memory (CNN-LSTM), and MobileNetv2 networks, for the assignment into different categories of the AQI, targeting the AQI prediction with the utilization of aerial images. Next, the second stage utilizes ANN, Recurrent Neural Networks (RNNs), LSTM, and RNN-B-LSTM popular deep machine learning techniques to develop a regression prediction system trained with the features and the resulting classification of air quality results from the highest accuracy approach of the first stage (which is the ANN approach with 99.14% accuracy). To tackle the issue of overfitting/underfitting, the oversampling technique (SMOTE) is used. The proposed method considers the contribution of each significant pollutant gas (i.e., PM2.5, PM10, NOx, NH3, SO2, O3) to the overall air quality index (AQI). The weather elements such as temperature and humidity impact air quality; they are also considered in our examination, resulting in a more accurate forecast. In addition, a comparison study is performed to identify the best model architecture for the proposed prediction system. Finally, the results show that the first stage of image classification should be executed with ANN (99.14 % accuracy), which is a highly accurate approach, and the second stage should be executed with the RNN-B-LSTM approach, again because of its high accuracy (98.73 % accuracy).

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来源期刊
Air Quality Atmosphere and Health
Air Quality Atmosphere and Health ENVIRONMENTAL SCIENCES-
CiteScore
8.80
自引率
2.00%
发文量
146
审稿时长
>12 weeks
期刊介绍: Air Quality, Atmosphere, and Health is a multidisciplinary journal which, by its very name, illustrates the broad range of work it publishes and which focuses on atmospheric consequences of human activities and their implications for human and ecological health. It offers research papers, critical literature reviews and commentaries, as well as special issues devoted to topical subjects or themes. International in scope, the journal presents papers that inform and stimulate a global readership, as the topic addressed are global in their import. Consequently, we do not encourage submission of papers involving local data that relate to local problems. Unless they demonstrate wide applicability, these are better submitted to national or regional journals. Air Quality, Atmosphere & Health addresses such topics as acid precipitation; airborne particulate matter; air quality monitoring and management; exposure assessment; risk assessment; indoor air quality; atmospheric chemistry; atmospheric modeling and prediction; air pollution climatology; climate change and air quality; air pollution measurement; atmospheric impact assessment; forest-fire emissions; atmospheric science; greenhouse gases; health and ecological effects; clean air technology; regional and global change and satellite measurements. This journal benefits a diverse audience of researchers, public health officials and policy makers addressing problems that call for solutions based in evidence from atmospheric and exposure assessment scientists, epidemiologists, and risk assessors. Publication in the journal affords the opportunity to reach beyond defined disciplinary niches to this broader readership.
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