利用装袋和 LSTM 神经网络,通过增强状态趋势意识和不确定性分析预测 PM2.5 浓度。

IF 2.2 4区 环境科学与生态学 Q3 ENVIRONMENTAL SCIENCES
Chao Bian, Guangqiu Huang
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引用次数: 0

摘要

监测空气污染物,尤其是 PM2.5(指直径为 2.5 μm 或更小的细颗粒物)已成为全球环境保护工作的重点。本研究引入了状态趋势感知的概念,这一概念被广泛应用于大数据分析,以增强全球威胁识别、理解和响应能力。我们将这种方法应用于 PM2.5 的预测,利用其在动态环境中提供整体见解和支持决策的能力。我们对大量历史数据进行了深入分析,以预测未来的浓度趋势。通过将长短期记忆(LSTM)神经网络与装袋集合学习算法相结合,与传统的 LSTM 和支持向量机(SVM)方法相比,我们开发的模型表现出更高的准确性和泛化能力,与 SVM-LSTM 相比,误差减少了 12%。我们进一步引入了区间预测来解决预测的不确定性,不仅提供了具体的 PM2.5,还预测了其变化的概率范围。模拟结果表明,我们的方法在提高预测准确性、增强模型泛化和减少过拟合方面非常有效,从而为环境监测和公共卫生决策提供了一个稳健的分析工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Predicting PM2.5 concentration with enhanced state–trend awareness and uncertainty analysis using bagging and LSTM neural networks

Monitoring air pollutants, particularly PM2.5, which refers to fine particulate matter with a diameter of 2.5 µm or smaller, has become a focal point of environmental protection efforts worldwide. This study introduces the concept of state–trend awareness, which is widely employed in big data analytics to enhance global threat identification, understanding, and response capabilities. We applied this approach to the prediction of PM2.5, utilizing its capacity to provide holistic insights and support decisions in dynamic environments. We conducted in-depth analyses of extensive historical data to forecast the future concentration trends. By combining a long short-term memory (LSTM) neural network with a bagging ensemble learning algorithm, our developed model demonstrated superior accuracy and generalization compared to those of traditional LSTM and support vector machine (SVM) methods, reducing errors relative to SVM-LSTM by 12%. We further introduced interval prediction to address forecasting uncertainties, not only providing a specific PM2.5 but also forecasting the probability ranges of its variations. The simulation results illustrate the effectiveness of our approach in improving the prediction accuracy, enhancing model generalization, and reducing overfitting, thereby offering a robust analytical tool for environmental monitoring and public health decision-making.

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来源期刊
Journal of environmental quality
Journal of environmental quality 环境科学-环境科学
CiteScore
4.90
自引率
8.30%
发文量
123
审稿时长
3 months
期刊介绍: Articles in JEQ cover various aspects of anthropogenic impacts on the environment, including agricultural, terrestrial, atmospheric, and aquatic systems, with emphasis on the understanding of underlying processes. To be acceptable for consideration in JEQ, a manuscript must make a significant contribution to the advancement of knowledge or toward a better understanding of existing concepts. The study should define principles of broad applicability, be related to problems over a sizable geographic area, or be of potential interest to a representative number of scientists. Emphasis is given to the understanding of underlying processes rather than to monitoring. Contributions are accepted from all disciplines for consideration by the editorial board. Manuscripts may be volunteered, invited, or coordinated as a special section or symposium.
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