An Autoregressive-Based Kalman Filter Approach for Daily PM2.5 Concentration Forecasting in Beijing, China.

IF 2.6 4区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Big Data Pub Date : 2024-02-01 Epub Date: 2023-05-03 DOI:10.1089/big.2022.0082
Xinyue Zhang, Chen Ding, Guizhi Wang
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引用次数: 0

Abstract

With the acceleration of urbanization, air pollution, especially PM2.5, has seriously affected human health and reduced people's life quality. Accurate PM2.5 prediction is significant for environmental protection authorities to take actions and develop prevention countermeasures. In this article, an adapted Kalman filter (KF) approach is presented to remove the nonlinearity and stochastic uncertainty of time series, suffered by the autoregressive integrated moving average (ARIMA) model. To further improve the accuracy of PM2.5 forecasting, a hybrid model is proposed by introducing an autoregressive (AR) model, where the AR part is used to determine the state-space equation, whereas the KF part is used for state estimation on PM2.5 concentration series. A modified artificial neural network (ANN), called AR-ANN is introduced to compare with the AR-KF model. According to the results, the AR-KF model outperforms the AR-ANN model and the original ARIMA model on the predication accuracy; that is, the AR-ANN obtains 10.85 and 15.45 of mean absolute error and root mean square error, respectively, whereas the ARIMA gains 30.58 and 29.39 on the corresponding metrics. It, therefore, proves that the presented AR-KF model can be adopted for air pollutant concentration prediction.

基于自回归卡尔曼滤波器的中国北京 PM2.5 每日浓度预测方法。
随着城市化进程的加快,空气污染尤其是 PM2.5 严重影响了人类健康,降低了人们的生活质量。准确预测 PM2.5 对环保部门采取行动和制定预防对策意义重大。本文提出了一种改进的卡尔曼滤波器(KF)方法,以消除自回归积分移动平均(ARIMA)模型所带来的时间序列的非线性和随机不确定性。为了进一步提高 PM2.5 预测的准确性,提出了一种混合模型,即引入自回归(AR)模型,其中 AR 部分用于确定状态空间方程,而 KF 部分用于 PM2.5 浓度序列的状态估计。为了与 AR-KF 模型进行比较,引入了一个名为 AR-ANN 的改进型人工神经网络(ANN)。结果表明,AR-KF 模型的预测精度优于 AR-ANN 模型和原始 ARIMA 模型,即 AR-ANN 模型的平均绝对误差和均方根误差分别为 10.85 和 15.45,而 ARIMA 模型的相应指标分别为 30.58 和 29.39。因此,这证明所提出的 AR-KF 模型可用于空气污染物浓度预测。
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来源期刊
Big Data
Big Data COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
9.10
自引率
2.20%
发文量
60
期刊介绍: Big Data is the leading peer-reviewed journal covering the challenges and opportunities in collecting, analyzing, and disseminating vast amounts of data. The Journal addresses questions surrounding this powerful and growing field of data science and facilitates the efforts of researchers, business managers, analysts, developers, data scientists, physicists, statisticians, infrastructure developers, academics, and policymakers to improve operations, profitability, and communications within their businesses and institutions. Spanning a broad array of disciplines focusing on novel big data technologies, policies, and innovations, the Journal brings together the community to address current challenges and enforce effective efforts to organize, store, disseminate, protect, manipulate, and, most importantly, find the most effective strategies to make this incredible amount of information work to benefit society, industry, academia, and government. Big Data coverage includes: Big data industry standards, New technologies being developed specifically for big data, Data acquisition, cleaning, distribution, and best practices, Data protection, privacy, and policy, Business interests from research to product, The changing role of business intelligence, Visualization and design principles of big data infrastructures, Physical interfaces and robotics, Social networking advantages for Facebook, Twitter, Amazon, Google, etc, Opportunities around big data and how companies can harness it to their advantage.
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