AIoT-driven multi-source sensor emission monitoring and forecasting using multi-source sensor integration with reduced noise series decomposition

Mughair Aslam Bhatti, Zhiyao Song, Uzair Aslam Bhatti, Syam M. S
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Abstract

The integration of multi-source sensors based AIoT (Artificial Intelligence of Things) technologies into air quality measurement and forecasting is becoming increasingly critical in the fields of sustainable and smart environmental design, urban development, and pollution control. This study focuses on enhancing the prediction of emission, with a special emphasis on pollutants, utilizing advanced deep learning (DL) techniques. Recurrent neural networks (RNNs) and long short-term memory (LSTM) neural networks have shown promise in predicting air quality trends in time series data. However, challenges persist due to the unpredictability of air quality data and the scarcity of long-term historical data for training. To address these challenges, this study introduces the AIoT-enhanced EEMD-CEEMDAN-GCN model. This innovative approach involves decomposing the input signal using EEMD (Ensemble Empirical Mode Decomposition) and CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) to extract intrinsic mode functions. These functions are then processed through a GCN (Graph Convolutional Network) model, enabling precise prediction of air quality trends. The model’s effectiveness is validated using air pollution datasets from four provinces in China, demonstrating its superiority over various deep learning models (GCN, EMD-GCN) and series decomposition models (EEMD-GCN, CEEMDAN-GCN). It achieves higher accuracy and better data fitting, outperforming other models in key metrics such as MAE (Mean Absolute Error), MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and R2 (Coefficient of Determination). The implementation of this AIoT-enhanced model in air pollution prediction allows decision-makers to more accurately anticipate changes in air quality, particularly concerning carbon emissions. This facilitates more effective planning of mitigation measures, improvement of public health, and optimization of resource allocation. Moreover, the model adeptly addresses the complexities of air quality data, contributing significantly to enhanced monitoring and management strategies in the context of sustainable urban development and environmental conservation.
利用多源传感器集成与降噪序列分解实现人工智能物联网驱动的多源传感器排放监测和预测
将基于多源传感器的 AIoT(人工智能物联网)技术整合到空气质量测量和预测中,在可持续智能环境设计、城市发展和污染控制领域正变得越来越重要。本研究的重点是利用先进的深度学习(DL)技术,加强对污染物排放的预测。递归神经网络(RNN)和长短期记忆(LSTM)神经网络在预测时间序列数据中的空气质量趋势方面已显示出良好的前景。然而,由于空气质量数据的不可预测性和用于训练的长期历史数据的稀缺性,挑战依然存在。为了应对这些挑战,本研究引入了 AIoT 增强型 EEMD-CEEMDAN-GCN 模型。这种创新方法包括使用 EEMD(集合经验模式分解)和 CEEMDAN(带自适应噪声的完全集合经验模式分解)对输入信号进行分解,以提取内在模式函数。然后通过 GCN(图形卷积网络)模型对这些函数进行处理,从而实现对空气质量趋势的精确预测。该模型的有效性通过中国四个省份的空气污染数据集进行了验证,证明其优于各种深度学习模型(GCN、EMD-GCN)和序列分解模型(EEMD-GCN、CEEMDAN-GCN)。它实现了更高的精度和更好的数据拟合,在 MAE(平均绝对误差)、MSE(平均平方误差)、MAPE(平均绝对百分比误差)和 R2(判定系数)等关键指标上优于其他模型。在空气污染预测中实施这一人工智能物联网增强型模型后,决策者可以更准确地预测空气质量的变化,尤其是碳排放方面的变化。这有助于更有效地规划缓解措施、改善公众健康和优化资源分配。此外,该模型还能巧妙地解决空气质量数据的复杂性问题,为在城市可持续发展和环境保护的背景下加强监测和管理策略做出了重要贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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