Multiview data fusion technique for missing value imputation in multisensory air pollution dataset

3区 计算机科学 Q1 Computer Science Journal of Ambient Intelligence and Humanized Computing Pub Date : 2024-06-04 DOI:10.1007/s12652-024-04816-9
Asif Iqbal Middya, Sarbani Roy
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Abstract

The missing readings in various sensors of air pollution monitoring stations is a common issue. Those missing sensor readings may greatly influence the performance of monitoring and analysis of air pollution data. To address this problem, in this paper, a multi-view based missing value (MV) imputation method called MVDI (Multi-View Data Imputation) is proposed for air pollution related time series data. MVDI combines four models namely LSTM (Long-Short Term Memory), IDS (Inverse Distance Squared), SVR (Support Vector Regressor), and KNN (K-Nearest Neighbors) to estimate MVs. These four models are mainly employed to capture the variations in data from different views of the dataset. Here, different views represent different portions (subsets) of the actual dataset. The estimates of MVs from all the views are combined using a kernel function to get an overall result. The proposed model MVDI is evaluated on real-world air pollution dataset in terms of RMSE, MAE, MAPE, and R2. The experimental results show that MVDI dominates over the baseline methods namely AR (AutoRegressive), ARIMA (AutoRegressive Integrated Moving Average), RFR (Random Forest Regressor), ANN (Artificial Neural Network), LI (Linear Interpolation), NN (Nearest Neighbors), MI (Mean Imputation), CNN (Convolutional Neural Network), ConvLSTM (Convolutional LSTM).

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用于多感官空气污染数据集缺失值估算的多视图数据融合技术
空气污染监测站的各种传感器读数缺失是一个常见问题。这些缺失的传感器读数可能会极大地影响空气污染数据的监测和分析性能。为解决这一问题,本文针对空气污染相关时间序列数据提出了一种基于多视图的缺失值(MV)估算方法,即 MVDI(多视图数据估算)。MVDI 结合了四种模型,即 LSTM(长短期记忆)、IDS(反距离平方)、SVR(支持向量回归器)和 KNN(K-近邻)来估计 MV。这四种模型主要用于捕捉数据集不同视图中的数据变化。这里,不同视图代表实际数据集的不同部分(子集)。使用核函数将所有视图的 MV 估计值进行组合,以得到整体结果。根据 RMSE、MAE、MAPE 和 R2,在实际空气污染数据集上对所提出的 MVDI 模型进行了评估。实验结果表明,MVDI 比 AR(自动回归法)、ARIMA(自动回归整合移动平均法)、RFR(随机森林回归法)、ANN(人工神经网络)、LI(线性插值法)、NN(近邻法)、MI(平均归约法)、CNN(卷积神经网络)、ConvLSTM(卷积 LSTM)等基线方法更具优势。
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来源期刊
Journal of Ambient Intelligence and Humanized Computing
Journal of Ambient Intelligence and Humanized Computing COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCEC-COMPUTER SCIENCE, INFORMATION SYSTEMS
CiteScore
9.60
自引率
0.00%
发文量
854
期刊介绍: The purpose of JAIHC is to provide a high profile, leading edge forum for academics, industrial professionals, educators and policy makers involved in the field to contribute, to disseminate the most innovative researches and developments of all aspects of ambient intelligence and humanized computing, such as intelligent/smart objects, environments/spaces, and systems. The journal discusses various technical, safety, personal, social, physical, political, artistic and economic issues. The research topics covered by the journal are (but not limited to): Pervasive/Ubiquitous Computing and Applications Cognitive wireless sensor network Embedded Systems and Software Mobile Computing and Wireless Communications Next Generation Multimedia Systems Security, Privacy and Trust Service and Semantic Computing Advanced Networking Architectures Dependable, Reliable and Autonomic Computing Embedded Smart Agents Context awareness, social sensing and inference Multi modal interaction design Ergonomics and product prototyping Intelligent and self-organizing transportation networks & services Healthcare Systems Virtual Humans & Virtual Worlds Wearables sensors and actuators
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